{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission\Hansen&Dinesen_Log-file_PSRM.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Jun 2021, 11:47:52

{com}. do "C:\Temp\STD00000000.tmp"
{txt}
{com}. 
. ** Replication Code for:
. 
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. 
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. 
. ** Date: 10/02/2021
. 
. 
. /*This study uses the following data file:
> Stefan Bauernschuster, Andreas Diekmann, Andreas Hadjar, Karin Kurz, 
> Ulrich Rosar, Ulrich Wagner, & Bettina Westle (2018). German General 
> Social Survey - ALLBUS 2016. GESIS Datenarchiv, Köln. ZA5252 Datenfile 
> Version 2.1.0 DE(2017), doi:10.4232/1.12837. 
> 
>         - To download the data file go to: https://www.gesis.org/home.
>         - Search for "ALLBUS 2016" or study number "ZA5250".
>         - In order to access the data you have to have a user account. You can
>           register for free.
>         - Select the single wave (non-comulative) data file, version 2.1.0 from 
>           29.05.2017, for Stata: make sure to select the German version of the file 
>           as the coding otherwise will not work correctly. */
. 
.         
. clear all
{txt}
{com}. set scheme s1mono
{txt}
{com}. set printcolor asis
{txt}
{com}. graph set window fontface "Times"
{txt}
{com}. 
. 
.                                 *CONTENTS
. 
. *Put in the path to the folder with the stored data files here:
. cd "C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"
{res}C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission
{txt}
{com}. set more off
{txt}
{com}.                         
. *To prepare variables run: 
. *do "2. Data preparation.do"
. 
. *To recreate main results run:
. do "3. Results.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. 
. *-------------------------------------------------------------------------------
. *3. Results for main text
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. 
. keep if german==1 & governmentsplit!=.                                  
{txt}(1,898 observations deleted)

{com}. 
. ********************************************************************************
. 
. 
.                                                                 ***Table 1***
. 
. *Full ethnocentrism index and civil liberties with reference to terrorism
. svy: regress st_terrori c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=.
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  11{txt},{res}    106{txt}){col 67}= {res}      1.57
{txt}{col 49}Prob > F{col 67}= {res}    0.1189
{txt}{col 49}R-squared{col 67}= {res}    0.0655

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1501529{col 48}{space 2} .0929733{col 59}{space 1}    1.62{col 68}{space 3}0.109{col 76}{space 4}-.0339924{col 89}{space 3} .3342982
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0025677{col 48}{space 2} .0379794{col 59}{space 1}   -0.07{col 68}{space 3}0.946{col 76}{space 4}-.0777907{col 89}{space 3} .0726553
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5848022{col 48}{space 2} .3411749{col 59}{space 1}    1.71{col 68}{space 3}0.089{col 76}{space 4}-.0909377{col 89}{space 3} 1.260542
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5101207{col 48}{space 2} .3801437{col 59}{space 1}   -1.34{col 68}{space 3}0.182{col 76}{space 4}-1.263043{col 89}{space 3} .2428019
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1233617{col 48}{space 2} .0712133{col 59}{space 1}   -1.73{col 68}{space 3}0.086{col 76}{space 4}-.2644086{col 89}{space 3} .0176851
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0583949{col 48}{space 2} .0828542{col 59}{space 1}   -0.70{col 68}{space 3}0.482{col 76}{space 4}-.2224982{col 89}{space 3} .1057083
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1282082{col 48}{space 2}  .081198{col 59}{space 1}   -1.58{col 68}{space 3}0.117{col 76}{space 4}-.2890312{col 89}{space 3} .0326147
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0432883{col 48}{space 2} .0790515{col 59}{space 1}   -0.55{col 68}{space 3}0.585{col 76}{space 4}-.1998598{col 89}{space 3} .1132832
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0730172{col 48}{space 2} .0942152{col 59}{space 1}   -0.78{col 68}{space 3}0.440{col 76}{space 4}-.2596223{col 89}{space 3}  .113588
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0062872{col 48}{space 2} .1222735{col 59}{space 1}    0.05{col 68}{space 3}0.959{col 76}{space 4}-.2358908{col 89}{space 3} .2484653
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0742073{col 48}{space 2} .1202974{col 59}{space 1}   -0.62{col 68}{space 3}0.539{col 76}{space 4}-.3124714{col 89}{space 3} .1640568
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5182031{col 48}{space 2} .1105567{col 59}{space 1}    4.69{col 68}{space 3}0.000{col 76}{space 4} .2992317{col 89}{space 3} .7371745
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  12{txt},{res}    105{txt}){col 67}= {res}      1.91
{txt}{col 49}Prob > F{col 67}= {res}    0.0412
{txt}{col 49}R-squared{col 67}= {res}    0.0930

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1406618{col 48}{space 2} .0913115{col 59}{space 1}    1.54{col 68}{space 3}0.126{col 76}{space 4}-.0401921{col 89}{space 3} .3215157
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1139349{col 48}{space 2} .0401856{col 59}{space 1}    2.84{col 68}{space 3}0.005{col 76}{space 4} .0343422{col 89}{space 3} .1935275
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} -.010819{col 48}{space 2} .0380031{col 59}{space 1}   -0.28{col 68}{space 3}0.776{col 76}{space 4}-.0860889{col 89}{space 3} .0644509
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .607384{col 48}{space 2} .3333252{col 59}{space 1}    1.82{col 68}{space 3}0.071{col 76}{space 4}-.0528085{col 89}{space 3} 1.267577
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5515108{col 48}{space 2} .3678571{col 59}{space 1}   -1.50{col 68}{space 3}0.137{col 76}{space 4}-1.280098{col 89}{space 3} .1770764
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1169071{col 48}{space 2} .0659235{col 59}{space 1}   -1.77{col 68}{space 3}0.079{col 76}{space 4}-.2474769{col 89}{space 3} .0136628
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0507394{col 48}{space 2} .0803026{col 59}{space 1}   -0.63{col 68}{space 3}0.529{col 76}{space 4}-.2097887{col 89}{space 3} .1083099
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1371289{col 48}{space 2} .0776694{col 59}{space 1}   -1.77{col 68}{space 3}0.080{col 76}{space 4}-.2909628{col 89}{space 3} .0167051
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0578587{col 48}{space 2} .0831142{col 59}{space 1}   -0.70{col 68}{space 3}0.488{col 76}{space 4}-.2224769{col 89}{space 3} .1067595
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0717269{col 48}{space 2} .0988857{col 59}{space 1}   -0.73{col 68}{space 3}0.470{col 76}{space 4}-.2675825{col 89}{space 3} .1241287
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}  .022431{col 48}{space 2} .1260644{col 59}{space 1}    0.18{col 68}{space 3}0.859{col 76}{space 4}-.2272555{col 89}{space 3} .2721176
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0816856{col 48}{space 2} .1208096{col 59}{space 1}   -0.68{col 68}{space 3}0.500{col 76}{space 4}-.3209643{col 89}{space 3}  .157593
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5083731{col 48}{space 2}  .108875{col 59}{space 1}    4.67{col 68}{space 3}0.000{col 76}{space 4} .2927324{col 89}{space 3} .7240138
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      4.88
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1330

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0007221{col 48}{space 2} .0977606{col 59}{space 1}    0.01{col 68}{space 3}0.994{col 76}{space 4}-.1929051{col 89}{space 3} .1943494
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1518117{col 48}{space 2}  .070707{col 59}{space 1}   -2.15{col 68}{space 3}0.034{col 76}{space 4}-.2918558{col 89}{space 3}-.0117676
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6750141{col 48}{space 2} .1507665{col 59}{space 1}    4.48{col 68}{space 3}0.000{col 76}{space 4}  .376402{col 89}{space 3} .9736263
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0061306{col 48}{space 2} .0367219{col 59}{space 1}   -0.17{col 68}{space 3}0.868{col 76}{space 4}-.0788629{col 89}{space 3} .0666017
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7003869{col 48}{space 2} .3186851{col 59}{space 1}    2.20{col 68}{space 3}0.030{col 76}{space 4} .0691909{col 89}{space 3} 1.331583
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6554761{col 48}{space 2} .3602407{col 59}{space 1}   -1.82{col 68}{space 3}0.071{col 76}{space 4}-1.368978{col 89}{space 3} .0580259
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0975267{col 48}{space 2} .0645064{col 59}{space 1}   -1.51{col 68}{space 3}0.133{col 76}{space 4}-.2252897{col 89}{space 3} .0302363
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}    -.022{col 48}{space 2} .0792046{col 59}{space 1}   -0.28{col 68}{space 3}0.782{col 76}{space 4}-.1788748{col 89}{space 3} .1348747
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1095112{col 48}{space 2} .0748847{col 59}{space 1}   -1.46{col 68}{space 3}0.146{col 76}{space 4}-.2578299{col 89}{space 3} .0388074
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0841434{col 48}{space 2} .0728254{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2283832{col 89}{space 3} .0600964
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1028858{col 48}{space 2} .0890782{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2793165{col 89}{space 3} .0735448
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0070997{col 48}{space 2} .1194672{col 59}{space 1}   -0.06{col 68}{space 3}0.953{col 76}{space 4}-.2437196{col 89}{space 3} .2295203
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1023694{col 48}{space 2} .1167093{col 59}{space 1}   -0.88{col 68}{space 3}0.382{col 76}{space 4}-.3335268{col 89}{space 3}  .128788
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5497256{col 48}{space 2}    .1046{col 59}{space 1}    5.26{col 68}{space 3}0.000{col 76}{space 4} .3425521{col 89}{space 3} .7568991
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. *Full ethnocentrism index and civil liberties without reference to terrorism
. svy: regress st_surveillanceindex c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       249
{txt}{col 1}Number of PSUs{col 20}= {res}      116{txt}{col 49}Population size{col 67}={res} 245.580911
{txt}{col 49}Design df{col 67}= {res}       115
{txt}{col 49}F({res}  11{txt},{res}    105{txt}){col 67}= {res}      3.97
{txt}{col 49}Prob > F{col 67}= {res}    0.0001
{txt}{col 49}R-squared{col 67}= {res}    0.1106

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}              st_surveillanceindex{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1658994{col 48}{space 2} .0926731{col 59}{space 1}    1.79{col 68}{space 3}0.076{col 76}{space 4}-.0176682{col 89}{space 3}  .349467
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0689045{col 48}{space 2} .0301505{col 59}{space 1}   -2.29{col 68}{space 3}0.024{col 76}{space 4}-.1286269{col 89}{space 3}-.0091821
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4322931{col 48}{space 2} .2867078{col 59}{space 1}    1.51{col 68}{space 3}0.134{col 76}{space 4}-.1356199{col 89}{space 3} 1.000206
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2643461{col 48}{space 2} .3182308{col 59}{space 1}   -0.83{col 68}{space 3}0.408{col 76}{space 4}   -.8947{col 89}{space 3} .3660078
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0302366{col 48}{space 2} .0600435{col 59}{space 1}   -0.50{col 68}{space 3}0.616{col 76}{space 4}-.1491713{col 89}{space 3} .0886981
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0500779{col 48}{space 2} .0605839{col 59}{space 1}    0.83{col 68}{space 3}0.410{col 76}{space 4}-.0699271{col 89}{space 3} .1700829
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0470596{col 48}{space 2} .0668738{col 59}{space 1}   -0.70{col 68}{space 3}0.483{col 76}{space 4}-.1795237{col 89}{space 3} .0854045
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0521103{col 48}{space 2} .0772411{col 59}{space 1}    0.67{col 68}{space 3}0.501{col 76}{space 4}-.1008894{col 89}{space 3} .2051101
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0370638{col 48}{space 2} .0819008{col 59}{space 1}    0.45{col 68}{space 3}0.652{col 76}{space 4}-.1251659{col 89}{space 3} .1992935
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1509977{col 48}{space 2} .1342967{col 59}{space 1}    1.12{col 68}{space 3}0.263{col 76}{space 4}-.1150183{col 89}{space 3} .4170136
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0643622{col 48}{space 2} .0849095{col 59}{space 1}    0.76{col 68}{space 3}0.450{col 76}{space 4}-.1038273{col 89}{space 3} .2325516
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .2206989{col 48}{space 2} .0891197{col 59}{space 1}    2.48{col 68}{space 3}0.015{col 76}{space 4} .0441698{col 89}{space 3} .3972279
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_surveillanceindex c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       249
{txt}{col 1}Number of PSUs{col 20}= {res}      116{txt}{col 49}Population size{col 67}={res} 245.580911
{txt}{col 49}Design df{col 67}= {res}       115
{txt}{col 49}F({res}  12{txt},{res}    104{txt}){col 67}= {res}      5.40
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1500

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}              st_surveillanceindex{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1577794{col 48}{space 2} .0932385{col 59}{space 1}    1.69{col 68}{space 3}0.093{col 76}{space 4}-.0269082{col 89}{space 3}  .342467
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1209177{col 48}{space 2} .0383475{col 59}{space 1}    3.15{col 68}{space 3}0.002{col 76}{space 4} .0449586{col 89}{space 3} .1968768
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0727966{col 48}{space 2} .0300061{col 59}{space 1}   -2.43{col 68}{space 3}0.017{col 76}{space 4} -.132233{col 89}{space 3}-.0133603
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4855974{col 48}{space 2} .2800014{col 59}{space 1}    1.73{col 68}{space 3}0.086{col 76}{space 4}-.0690314{col 89}{space 3} 1.040226
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.3386412{col 48}{space 2} .3159996{col 59}{space 1}   -1.07{col 68}{space 3}0.286{col 76}{space 4}-.9645756{col 89}{space 3} .2872933
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0299656{col 48}{space 2} .0569168{col 59}{space 1}   -0.53{col 68}{space 3}0.600{col 76}{space 4}-.1427068{col 89}{space 3} .0827756
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0522146{col 48}{space 2} .0591151{col 59}{space 1}    0.88{col 68}{space 3}0.379{col 76}{space 4} -.064881{col 89}{space 3} .1693103
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}  -.06093{col 48}{space 2} .0649052{col 59}{space 1}   -0.94{col 68}{space 3}0.350{col 76}{space 4}-.1894948{col 89}{space 3} .0676348
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0398564{col 48}{space 2} .0808809{col 59}{space 1}    0.49{col 68}{space 3}0.623{col 76}{space 4}-.1203532{col 89}{space 3}  .200066
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0380864{col 48}{space 2} .0863007{col 59}{space 1}    0.44{col 68}{space 3}0.660{col 76}{space 4}-.1328586{col 89}{space 3} .2090315
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1672217{col 48}{space 2} .1379936{col 59}{space 1}    1.21{col 68}{space 3}0.228{col 76}{space 4} -.106117{col 89}{space 3} .4405603
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0641553{col 48}{space 2} .0865277{col 59}{space 1}    0.74{col 68}{space 3}0.460{col 76}{space 4}-.1072393{col 89}{space 3} .2355499
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .2062342{col 48}{space 2} .0889083{col 59}{space 1}    2.32{col 68}{space 3}0.022{col 76}{space 4} .0301239{col 89}{space 3} .3823445
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_surveillanceindex c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       249
{txt}{col 1}Number of PSUs{col 20}= {res}      116{txt}{col 49}Population size{col 67}={res} 245.580911
{txt}{col 49}Design df{col 67}= {res}       115
{txt}{col 49}F({res}  13{txt},{res}    103{txt}){col 67}= {res}      5.10
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1502

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}              st_surveillanceindex{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1503286{col 48}{space 2} .1050585{col 59}{space 1}    1.43{col 68}{space 3}0.155{col 76}{space 4}-.0577721{col 89}{space 3} .3584294
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1069277{col 48}{space 2} .0756068{col 59}{space 1}    1.41{col 68}{space 3}0.160{col 76}{space 4}-.0428349{col 89}{space 3} .2566903
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .0365302{col 48}{space 2} .1699852{col 59}{space 1}    0.21{col 68}{space 3}0.830{col 76}{space 4}-.3001778{col 89}{space 3} .3732382
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}  -.07246{col 48}{space 2} .0301241{col 59}{space 1}   -2.41{col 68}{space 3}0.018{col 76}{space 4}-.1321301{col 89}{space 3}-.0127898
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4911836{col 48}{space 2} .2830537{col 59}{space 1}    1.74{col 68}{space 3}0.085{col 76}{space 4}-.0694912{col 89}{space 3} 1.051859
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.3446094{col 48}{space 2} .3195979{col 59}{space 1}   -1.08{col 68}{space 3}0.283{col 76}{space 4}-.9776712{col 89}{space 3} .2884525
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0292649{col 48}{space 2} .0563266{col 59}{space 1}   -0.52{col 68}{space 3}0.604{col 76}{space 4}-.1408371{col 89}{space 3} .0823072
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0532519{col 48}{space 2} .0585186{col 59}{space 1}    0.91{col 68}{space 3}0.365{col 76}{space 4}-.0626623{col 89}{space 3}  .169166
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0599279{col 48}{space 2} .0639056{col 59}{space 1}   -0.94{col 68}{space 3}0.350{col 76}{space 4}-.1865125{col 89}{space 3} .0666567
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0385478{col 48}{space 2}  .080527{col 59}{space 1}    0.48{col 68}{space 3}0.633{col 76}{space 4}-.1209606{col 89}{space 3} .1980563
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0363332{col 48}{space 2} .0862067{col 59}{space 1}    0.42{col 68}{space 3}0.674{col 76}{space 4}-.1344257{col 89}{space 3}  .207092
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1655713{col 48}{space 2} .1379152{col 59}{space 1}    1.20{col 68}{space 3}0.232{col 76}{space 4}-.1076121{col 89}{space 3} .4387547
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0628653{col 48}{space 2} .0860476{col 59}{space 1}    0.73{col 68}{space 3}0.467{col 76}{space 4}-.1075784{col 89}{space 3}  .233309
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .208489{col 48}{space 2} .0896122{col 59}{space 1}    2.33{col 68}{space 3}0.022{col 76}{space 4} .0309845{col 89}{space 3} .3859935
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. *Authorchild and civil liberties with reference to terrorism
. svy: regress st_terrori c.st_authorchild i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       271
{txt}{col 1}Number of PSUs{col 20}= {res}      122{txt}{col 49}Population size{col 67}={res} 267.121285
{txt}{col 49}Design df{col 67}= {res}       121
{txt}{col 49}F({res}  11{txt},{res}    111{txt}){col 67}= {res}      1.45
{txt}{col 49}Prob > F{col 67}= {res}    0.1598
{txt}{col 49}R-squared{col 67}= {res}    0.0505

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.0587124{col 48}{space 2} .0767007{col 59}{space 1}   -0.77{col 68}{space 3}0.445{col 76}{space 4}-.2105618{col 89}{space 3} .0931369
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0072634{col 48}{space 2} .0361849{col 59}{space 1}    0.20{col 68}{space 3}0.841{col 76}{space 4}-.0643741{col 89}{space 3} .0789009
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .3993989{col 48}{space 2}  .308688{col 59}{space 1}    1.29{col 68}{space 3}0.198{col 76}{space 4}-.2117305{col 89}{space 3} 1.010528
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2566153{col 48}{space 2}  .353539{col 59}{space 1}   -0.73{col 68}{space 3}0.469{col 76}{space 4} -.956539{col 89}{space 3} .4433084
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1520509{col 48}{space 2} .0695769{col 59}{space 1}   -2.19{col 68}{space 3}0.031{col 76}{space 4}-.2897968{col 89}{space 3}-.0143051
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1009689{col 48}{space 2} .0815209{col 59}{space 1}   -1.24{col 68}{space 3}0.218{col 76}{space 4}-.2623611{col 89}{space 3} .0604232
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1678526{col 48}{space 2} .0779175{col 59}{space 1}   -2.15{col 68}{space 3}0.033{col 76}{space 4}-.3221109{col 89}{space 3}-.0135944
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0032526{col 48}{space 2} .0790354{col 59}{space 1}   -0.04{col 68}{space 3}0.967{col 76}{space 4} -.159724{col 89}{space 3} .1532188
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0410558{col 48}{space 2} .0940399{col 59}{space 1}   -0.44{col 68}{space 3}0.663{col 76}{space 4}-.2272326{col 89}{space 3} .1451209
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0392141{col 48}{space 2} .1188507{col 59}{space 1}    0.33{col 68}{space 3}0.742{col 76}{space 4}-.1960822{col 89}{space 3} .2745104
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} -.049976{col 48}{space 2} .1183155{col 59}{space 1}   -0.42{col 68}{space 3}0.673{col 76}{space 4}-.2842128{col 89}{space 3} .1842607
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5940497{col 48}{space 2} .1011948{col 59}{space 1}    5.87{col 68}{space 3}0.000{col 76}{space 4} .3937078{col 89}{space 3} .7943915
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_authorchild i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       271
{txt}{col 1}Number of PSUs{col 20}= {res}      122{txt}{col 49}Population size{col 67}={res} 267.121285
{txt}{col 49}Design df{col 67}= {res}       121
{txt}{col 49}F({res}  12{txt},{res}    110{txt}){col 67}= {res}      1.57
{txt}{col 49}Prob > F{col 67}= {res}    0.1113
{txt}{col 49}R-squared{col 67}= {res}    0.0731

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.0690424{col 48}{space 2} .0744163{col 59}{space 1}   -0.93{col 68}{space 3}0.355{col 76}{space 4} -.216369{col 89}{space 3} .0782842
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}  .102895{col 48}{space 2} .0406592{col 59}{space 1}    2.53{col 68}{space 3}0.013{col 76}{space 4} .0223995{col 89}{space 3} .1833906
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0004791{col 48}{space 2} .0367121{col 59}{space 1}   -0.01{col 68}{space 3}0.990{col 76}{space 4}-.0731604{col 89}{space 3} .0722021
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4212615{col 48}{space 2} .2994339{col 59}{space 1}    1.41{col 68}{space 3}0.162{col 76}{space 4}-.1715468{col 89}{space 3}  1.01407
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2929628{col 48}{space 2} .3406482{col 59}{space 1}   -0.86{col 68}{space 3}0.391{col 76}{space 4}-.9673657{col 89}{space 3} .3814401
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1479134{col 48}{space 2}  .064853{col 59}{space 1}   -2.28{col 68}{space 3}0.024{col 76}{space 4}-.2763069{col 89}{space 3}-.0195198
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0972703{col 48}{space 2} .0801105{col 59}{space 1}   -1.21{col 68}{space 3}0.227{col 76}{space 4}-.2558703{col 89}{space 3} .0613296
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} -.177376{col 48}{space 2} .0752346{col 59}{space 1}   -2.36{col 68}{space 3}0.020{col 76}{space 4}-.3263228{col 89}{space 3}-.0284293
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0106801{col 48}{space 2} .0805134{col 59}{space 1}   -0.13{col 68}{space 3}0.895{col 76}{space 4}-.1700775{col 89}{space 3} .1487173
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} -.035123{col 48}{space 2} .0963119{col 59}{space 1}   -0.36{col 68}{space 3}0.716{col 76}{space 4}-.2257978{col 89}{space 3} .1555518
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0581427{col 48}{space 2} .1213039{col 59}{space 1}    0.48{col 68}{space 3}0.633{col 76}{space 4}-.1820104{col 89}{space 3} .2982957
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} -.048504{col 48}{space 2} .1178654{col 59}{space 1}   -0.41{col 68}{space 3}0.681{col 76}{space 4}-.2818496{col 89}{space 3} .1848415
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5793909{col 48}{space 2} .0987595{col 59}{space 1}    5.87{col 68}{space 3}0.000{col 76}{space 4} .3838705{col 89}{space 3} .7749113
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_authorchild##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       271
{txt}{col 1}Number of PSUs{col 20}= {res}      122{txt}{col 49}Population size{col 67}={res} 267.121285
{txt}{col 49}Design df{col 67}= {res}       121
{txt}{col 49}F({res}  13{txt},{res}    109{txt}){col 67}= {res}      1.57
{txt}{col 49}Prob > F{col 67}= {res}    0.1058
{txt}{col 49}R-squared{col 67}= {res}    0.0777

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.1121539{col 48}{space 2} .0906356{col 59}{space 1}   -1.24{col 68}{space 3}0.218{col 76}{space 4} -.291591{col 89}{space 3} .0672831
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .0679622{col 48}{space 2} .0536574{col 59}{space 1}    1.27{col 68}{space 3}0.208{col 76}{space 4}-.0382667{col 89}{space 3} .1741911
{txt}{space 34} {c |}
{space 7}treatment1#c.st_authorchild {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1713973{col 48}{space 2} .1430185{col 59}{space 1}    1.20{col 68}{space 3}0.233{col 76}{space 4}-.1117455{col 89}{space 3} .4545402
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0022375{col 48}{space 2} .0362973{col 59}{space 1}   -0.06{col 68}{space 3}0.951{col 76}{space 4}-.0740976{col 89}{space 3} .0696227
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4199125{col 48}{space 2} .2991269{col 59}{space 1}    1.40{col 68}{space 3}0.163{col 76}{space 4} -.172288{col 89}{space 3} 1.012113
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2994846{col 48}{space 2} .3401666{col 59}{space 1}   -0.88{col 68}{space 3}0.380{col 76}{space 4}-.9729341{col 89}{space 3} .3739649
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1505292{col 48}{space 2} .0643505{col 59}{space 1}   -2.34{col 68}{space 3}0.021{col 76}{space 4}-.2779279{col 89}{space 3}-.0231305
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1009117{col 48}{space 2} .0791246{col 59}{space 1}   -1.28{col 68}{space 3}0.205{col 76}{space 4}-.2575599{col 89}{space 3} .0557364
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1755219{col 48}{space 2} .0740207{col 59}{space 1}   -2.37{col 68}{space 3}0.019{col 76}{space 4}-.3220654{col 89}{space 3}-.0289784
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0156779{col 48}{space 2} .0803402{col 59}{space 1}   -0.20{col 68}{space 3}0.846{col 76}{space 4}-.1747325{col 89}{space 3} .1433767
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0397308{col 48}{space 2}  .095882{col 59}{space 1}   -0.41{col 68}{space 3}0.679{col 76}{space 4}-.2295545{col 89}{space 3} .1500928
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0498903{col 48}{space 2}  .120984{col 59}{space 1}    0.41{col 68}{space 3}0.681{col 76}{space 4}-.1896293{col 89}{space 3}   .28941
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0608655{col 48}{space 2} .1205295{col 59}{space 1}   -0.50{col 68}{space 3}0.614{col 76}{space 4}-.2994854{col 89}{space 3} .1777545
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5965771{col 48}{space 2} .1011629{col 59}{space 1}    5.90{col 68}{space 3}0.000{col 76}{space 4} .3962984{col 89}{space 3} .7968557
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. *Interaction model including both ethnocentrism and authoritarianism
. svy: regress st_terrori c.st_ethno##i.treatment1 c.st_authorchild##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  15{txt},{res}    102{txt}){col 67}= {res}      4.59
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1472

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0191412{col 48}{space 2} .0969402{col 59}{space 1}    0.20{col 68}{space 3}0.844{col 76}{space 4}-.1728611{col 89}{space 3} .2111435
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1671138{col 48}{space 2}  .071321{col 59}{space 1}   -2.34{col 68}{space 3}0.021{col 76}{space 4}-.3083741{col 89}{space 3}-.0258535
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .7634099{col 48}{space 2} .1643876{col 59}{space 1}    4.64{col 68}{space 3}0.000{col 76}{space 4} .4378195{col 89}{space 3}    1.089
{txt}{space 34} {c |}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.1198122{col 48}{space 2}  .090811{col 59}{space 1}   -1.32{col 68}{space 3}0.190{col 76}{space 4}-.2996747{col 89}{space 3} .0600504
{txt}{space 34} {c |}
{space 7}treatment1#c.st_authorchild {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.0699534{col 48}{space 2} .1477699{col 59}{space 1}   -0.47{col 68}{space 3}0.637{col 76}{space 4}-.3626303{col 89}{space 3} .2227236
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0102722{col 48}{space 2}  .035883{col 59}{space 1}   -0.29{col 68}{space 3}0.775{col 76}{space 4} -.081343{col 89}{space 3} .0607987
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6778936{col 48}{space 2} .3073803{col 59}{space 1}    2.21{col 68}{space 3}0.029{col 76}{space 4} .0690882{col 89}{space 3} 1.286699
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5973839{col 48}{space 2} .3435271{col 59}{space 1}   -1.74{col 68}{space 3}0.085{col 76}{space 4}-1.277783{col 89}{space 3} .0830149
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1005896{col 48}{space 2} .0624354{col 59}{space 1}   -1.61{col 68}{space 3}0.110{col 76}{space 4}-.2242508{col 89}{space 3} .0230716
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} -.027705{col 48}{space 2} .0791434{col 59}{space 1}   -0.35{col 68}{space 3}0.727{col 76}{space 4}-.1844585{col 89}{space 3} .1290486
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1104627{col 48}{space 2} .0721075{col 59}{space 1}   -1.53{col 68}{space 3}0.128{col 76}{space 4}-.2532808{col 89}{space 3} .0323554
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0896947{col 48}{space 2} .0751877{col 59}{space 1}   -1.19{col 68}{space 3}0.235{col 76}{space 4}-.2386134{col 89}{space 3} .0592239
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1136275{col 48}{space 2} .0899511{col 59}{space 1}   -1.26{col 68}{space 3}0.209{col 76}{space 4} -.291787{col 89}{space 3}  .064532
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0185951{col 48}{space 2}  .119556{col 59}{space 1}   -0.16{col 68}{space 3}0.877{col 76}{space 4}-.2553908{col 89}{space 3} .2182006
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0808321{col 48}{space 2}  .124065{col 59}{space 1}   -0.65{col 68}{space 3}0.516{col 76}{space 4}-.3265585{col 89}{space 3} .1648942
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5684138{col 48}{space 2} .1081742{col 59}{space 1}    5.25{col 68}{space 3}0.000{col 76}{space 4}  .354161{col 89}{space 3} .7826665
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. 
. 
. 
.                         ****Interaction effect for different levels of ethnocentrism***
.                         
. *Calculate percentiles based on sample used in the analyses (see below). 
. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      4.88
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1330

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0007221{col 48}{space 2} .0977606{col 59}{space 1}    0.01{col 68}{space 3}0.994{col 76}{space 4}-.1929051{col 89}{space 3} .1943494
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1518117{col 48}{space 2}  .070707{col 59}{space 1}   -2.15{col 68}{space 3}0.034{col 76}{space 4}-.2918558{col 89}{space 3}-.0117676
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6750141{col 48}{space 2} .1507665{col 59}{space 1}    4.48{col 68}{space 3}0.000{col 76}{space 4}  .376402{col 89}{space 3} .9736263
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0061306{col 48}{space 2} .0367219{col 59}{space 1}   -0.17{col 68}{space 3}0.868{col 76}{space 4}-.0788629{col 89}{space 3} .0666017
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7003869{col 48}{space 2} .3186851{col 59}{space 1}    2.20{col 68}{space 3}0.030{col 76}{space 4} .0691909{col 89}{space 3} 1.331583
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6554761{col 48}{space 2} .3602407{col 59}{space 1}   -1.82{col 68}{space 3}0.071{col 76}{space 4}-1.368978{col 89}{space 3} .0580259
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0975267{col 48}{space 2} .0645064{col 59}{space 1}   -1.51{col 68}{space 3}0.133{col 76}{space 4}-.2252897{col 89}{space 3} .0302363
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}    -.022{col 48}{space 2} .0792046{col 59}{space 1}   -0.28{col 68}{space 3}0.782{col 76}{space 4}-.1788748{col 89}{space 3} .1348747
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1095112{col 48}{space 2} .0748847{col 59}{space 1}   -1.46{col 68}{space 3}0.146{col 76}{space 4}-.2578299{col 89}{space 3} .0388074
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0841434{col 48}{space 2} .0728254{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2283832{col 89}{space 3} .0600964
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1028858{col 48}{space 2} .0890782{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2793165{col 89}{space 3} .0735448
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0070997{col 48}{space 2} .1194672{col 59}{space 1}   -0.06{col 68}{space 3}0.953{col 76}{space 4}-.2437196{col 89}{space 3} .2295203
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1023694{col 48}{space 2} .1167093{col 59}{space 1}   -0.88{col 68}{space 3}0.382{col 76}{space 4}-.3335268{col 89}{space 3}  .128788
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5497256{col 48}{space 2}    .1046{col 59}{space 1}    5.26{col 68}{space 3}0.000{col 76}{space 4} .3425521{col 89}{space 3} .7568991
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. sum st_ethno if e(sample), detail

                          {txt}st_ethno
{hline 61}
      Percentiles      Smallest
 1%    {res}      .05              0
{txt} 5%    {res} .1166667       .0333333
{txt}10%    {res} .1583333            .05       {txt}Obs         {res}        254
{txt}25%    {res}      .25       .0583333       {txt}Sum of Wgt. {res}        254

{txt}50%    {res} .3833333                      {txt}Mean          {res}  .397605
                        {txt}Largest       Std. Dev.     {res} .1995411
{txt}75%    {res} .5333334             .9
{txt}90%    {res}      .65             .9       {txt}Variance      {res} .0398166
{txt}95%    {res}     .775       .9166667       {txt}Skewness      {res} .5145561
{txt}99%    {res}       .9              1       {txt}Kurtosis      {res}  2.86251
{txt}
{com}. 
. *Interaction effect for different levels of ethnocentrism for pre- and post-attacks
. *groups 
. *0, 50, and 95th percentiles, control and treatment
. margins, at(treatment1=(0) st_ethno=(0.1166667 .3833333 0.775)) vce(unconditional)
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       254

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.1166667}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.3833333}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.775}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5322032{col 26}{space 2} .0335529{col 37}{space 1}   15.86{col 46}{space 3}0.000{col 54}{space 4} .4657476{col 67}{space 3} .5986589
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5323958{col 26}{space 2} .0218959{col 37}{space 1}   24.31{col 46}{space 3}0.000{col 54}{space 4} .4890283{col 67}{space 3} .5757633
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .5326786{col 26}{space 2} .0446583{col 37}{space 1}   11.93{col 46}{space 3}0.000{col 54}{space 4} .4442273{col 67}{space 3}   .62113
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(treatment1=(1) st_ethno=(0.1166667 .3833333 0.775)) vce(unconditional)
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       254

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.1166667}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.3833333}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.775}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4591432{col 26}{space 2} .0488295{col 37}{space 1}    9.40{col 46}{space 3}0.000{col 54}{space 4} .3624303{col 67}{space 3} .5558561
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6393395{col 26}{space 2} .0326736{col 37}{space 1}   19.57{col 46}{space 3}0.000{col 54}{space 4} .5746254{col 67}{space 3} .7040536
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .9040029{col 26}{space 2} .0597235{col 37}{space 1}   15.14{col 46}{space 3}0.000{col 54}{space 4} .7857129{col 67}{space 3} 1.022293
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. di .9040029-.4591432
{res}.4448597
{txt}
{com}. 
. *Difference/change from pre-attacks to post-attacks group across different 
. *ethnocentrism levels 
. margins, dydx(treatment1) at(st_ethno=(0.1166667))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}       254
{txt}Model VCE{col 14}: {res}Linearized

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.treatment1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.1166667}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}treatment1 {c |}
{space 2}Treatment  {c |}{col 14}{res}{space 2}  -.07306{col 26}{space 2} .0568821{col 37}{space 1}   -1.28{col 46}{space 3}0.202{col 54}{space 4}-.1857223{col 67}{space 3} .0396022
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. margins, dydx(treatment1) at(st_ethno=(.38333))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}       254
{txt}Model VCE{col 14}: {res}Linearized

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.treatment1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 5}.38333}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}treatment1 {c |}
{space 2}Treatment  {c |}{col 14}{res}{space 2} .1069414{col 26}{space 2} .0390817{col 37}{space 1}    2.74{col 46}{space 3}0.007{col 54}{space 4} .0295353{col 67}{space 3} .1843476
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. margins, dydx(treatment1) at(st_ethno=(0.775))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}       254
{txt}Model VCE{col 14}: {res}Linearized

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.treatment1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.775}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}treatment1 {c |}
{space 2}Treatment  {c |}{col 14}{res}{space 2} .3713242{col 26}{space 2} .0698531{col 37}{space 1}    5.32{col 46}{space 3}0.000{col 54}{space 4} .2329713{col 67}{space 3} .5096772
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. 
. 
.                                                                 ****Figure 1***
. 
. *Marginsplot for different percentiles on the ethnocentrism variable 
. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      4.88
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1330

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0007221{col 48}{space 2} .0977606{col 59}{space 1}    0.01{col 68}{space 3}0.994{col 76}{space 4}-.1929051{col 89}{space 3} .1943494
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1518117{col 48}{space 2}  .070707{col 59}{space 1}   -2.15{col 68}{space 3}0.034{col 76}{space 4}-.2918558{col 89}{space 3}-.0117676
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6750141{col 48}{space 2} .1507665{col 59}{space 1}    4.48{col 68}{space 3}0.000{col 76}{space 4}  .376402{col 89}{space 3} .9736263
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0061306{col 48}{space 2} .0367219{col 59}{space 1}   -0.17{col 68}{space 3}0.868{col 76}{space 4}-.0788629{col 89}{space 3} .0666017
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7003869{col 48}{space 2} .3186851{col 59}{space 1}    2.20{col 68}{space 3}0.030{col 76}{space 4} .0691909{col 89}{space 3} 1.331583
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6554761{col 48}{space 2} .3602407{col 59}{space 1}   -1.82{col 68}{space 3}0.071{col 76}{space 4}-1.368978{col 89}{space 3} .0580259
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0975267{col 48}{space 2} .0645064{col 59}{space 1}   -1.51{col 68}{space 3}0.133{col 76}{space 4}-.2252897{col 89}{space 3} .0302363
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}    -.022{col 48}{space 2} .0792046{col 59}{space 1}   -0.28{col 68}{space 3}0.782{col 76}{space 4}-.1788748{col 89}{space 3} .1348747
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1095112{col 48}{space 2} .0748847{col 59}{space 1}   -1.46{col 68}{space 3}0.146{col 76}{space 4}-.2578299{col 89}{space 3} .0388074
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0841434{col 48}{space 2} .0728254{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2283832{col 89}{space 3} .0600964
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1028858{col 48}{space 2} .0890782{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2793165{col 89}{space 3} .0735448
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0070997{col 48}{space 2} .1194672{col 59}{space 1}   -0.06{col 68}{space 3}0.953{col 76}{space 4}-.2437196{col 89}{space 3} .2295203
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1023694{col 48}{space 2} .1167093{col 59}{space 1}   -0.88{col 68}{space 3}0.382{col 76}{space 4}-.3335268{col 89}{space 3}  .128788
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5497256{col 48}{space 2}    .1046{col 59}{space 1}    5.26{col 68}{space 3}0.000{col 76}{space 4} .3425521{col 89}{space 3} .7568991
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. sum st_ethno if e(sample), detail

                          {txt}st_ethno
{hline 61}
      Percentiles      Smallest
 1%    {res}      .05              0
{txt} 5%    {res} .1166667       .0333333
{txt}10%    {res} .1583333            .05       {txt}Obs         {res}        254
{txt}25%    {res}      .25       .0583333       {txt}Sum of Wgt. {res}        254

{txt}50%    {res} .3833333                      {txt}Mean          {res}  .397605
                        {txt}Largest       Std. Dev.     {res} .1995411
{txt}75%    {res} .5333334             .9
{txt}90%    {res}      .65             .9       {txt}Variance      {res} .0398166
{txt}95%    {res}     .775       .9166667       {txt}Skewness      {res} .5145561
{txt}99%    {res}       .9              1       {txt}Kurtosis      {res}  2.86251
{txt}
{com}. margins, at( treatment1=(0,1) st_ethno=(0.1166667, .3833333, 0.775)) vce(unconditional)
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       254

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.1166667}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.1166667}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.3833333}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.3833333}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.775}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.775}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5322032{col 26}{space 2} .0335529{col 37}{space 1}   15.86{col 46}{space 3}0.000{col 54}{space 4} .4657476{col 67}{space 3} .5986589
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .4591432{col 26}{space 2} .0488295{col 37}{space 1}    9.40{col 46}{space 3}0.000{col 54}{space 4} .3624303{col 67}{space 3} .5558561
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .5323958{col 26}{space 2} .0218959{col 37}{space 1}   24.31{col 46}{space 3}0.000{col 54}{space 4} .4890283{col 67}{space 3} .5757633
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .6393395{col 26}{space 2} .0326736{col 37}{space 1}   19.57{col 46}{space 3}0.000{col 54}{space 4} .5746254{col 67}{space 3} .7040536
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .5326786{col 26}{space 2} .0446583{col 37}{space 1}   11.93{col 46}{space 3}0.000{col 54}{space 4} .4442273{col 67}{space 3}   .62113
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .9040029{col 26}{space 2} .0597235{col 37}{space 1}   15.14{col 46}{space 3}0.000{col 54}{space 4} .7857129{col 67}{space 3} 1.022293
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.  marginsplot, xdimension(st_ethno treatment1, ///
>  elabels(1 "5th percentile" 2 " " 3 "50th percentile" 4 " " 5 "95th percentile" 6 " " )) ///
>  horizontal plotopts(connect(none)) ytitle("") yscale(reverse) yline(2.5 4.5, ///
>  lwidth(vvthin)) ylabel(, grid glcolor(gs14)) xtitle("") ///
>  xlabel(, grid glcolor(gs14)) xmtick(##2, grid glcolor(gs14)) ///
>  title("""", size(medium)) ///
>  legend(on order(2 "Predicted mean for pre-attacks group" 1 "95 % confidence interval") cols(1) region(lcolor(none)))

{text}{p 2 6 2}Variables that uniquely identify margins: treatment1 st_ethno{p_end}
{res}{txt}
{com}.  
. *Change symbol of the post-attacks group in the graph-editor.
. 
. 
. 
.  
. 
{txt}end of do-file

{com}. 
. *For additional analyses run:
. do "Appendix_B.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. 
. *-------------------------------------------------------------------------------
. *Appendix B: Time frame and studied sample
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. 
. 
. ********************************************************************************
. 
. 
.                                                           ***B1 Time frame***
. 
.                                                                 ***Figure B1***
.                                                                 
. *Timing of high-profile terrorist attacks during the fieldwork period 
. gen reflines=0
{txt}
{com}. replace reflines = 100 if edate==td(09/01/2015) /*Paris january*/
{txt}(0 real changes made)

{com}. replace reflines = 100 if edate==td(13/11/2015) /*Paris November*/
{txt}(0 real changes made)

{com}. replace reflines = 100 if edate==td(31/12/2015) /*New Year's*/
{txt}(0 real changes made)

{com}. replace reflines = 100 if edate==td(22/03/2016) /*Brussels*/
{txt}(0 real changes made)

{com}. replace reflines = 100 if edate==td(12/06/2016) /*Orlando*/
{txt}(2 real changes made)

{com}. replace reflines = 100 if edate==td(14/07/2016) /*Nice*/
{txt}(26 real changes made)

{com}. replace reflines = 100 if edate==td(18/07/2016) /*Würzburg*/
{txt}(26 real changes made)

{com}. replace reflines = 100 if edate==td(22/07/2016) /*Munich*/
{txt}(19 real changes made)

{com}. replace reflines = 100 if edate==td(24/07/2016) /*Ansbach*/
{txt}(0 real changes made)

{com}. replace reflines = 100 if edate==td(26/07/2016) /*Normandy*/
{txt}(22 real changes made)

{com}. replace reflines = 100 if edate==td(19/12/2016) /*Berlin*/
{txt}(0 real changes made)

{com}. 
.         
. twoway (bar Interviews edate if edate>=td(15/03/2016) & edate<=td(18/09/2016), fcolor(white) lcolor(gs13)) ///
>         (bar Interviews edate if edate>=td(24/06/2016) & edate<=td(13/07/2016), fcolor(gs14) lcolor(gs9)) ///
>         (bar Interviews edate if edate>=td(27/07/2016) & edate<=td(02/08/2016), fcolor(gs8) lcolor(gs5)) ///
>         (spike reflines edate if edate>=td(20/03/2016) & edate<=td(28/09/2016), lcolor(black)), ///
>         ytitle(Interviews per day (n)) ytitle(, size(msmall) margin(small)) ///
>         ylabel(#6, labsize(msmall) angle(horizontal) labgap(tiny) grid) xtitle("") ///
>         xlabel(#7, labsize(msmall) angle(stdarrow)) ///
>         legend(order(2 "Pre-attacks group: 24 June to 13 July, n=238" 3 "Post-attacks group: 27 July to 2 August, n=59" 4 "High-profile terror attacks") cols(1) ///
>         rowgap(small) size(msmall) region(lcolor(none)))
{p 0 4 2}
{txt}(note:  named style
msmall not found in class
gsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
msmall not found in class
gsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
msmall not found in class
gsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
msmall not found in class
gsize,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. drop reflines
{txt}
{com}. ********************************************************************************
. 
. 
.                                                         ***B2 Studied sample***
.                                                         
. keep if governmentsplit==1 & treatment1!=. & german==1
{txt}(3,193 observations deleted)

{com}. 
. *All respondends in studied subsample 
. tab treatment1

 {txt}treatment1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
    Control {c |}{res}        238       80.13       80.13
{txt}  Treatment {c |}{res}         59       19.87      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        297      100.00
{txt}
{com}. tab sex if treatment1!=.

  {txt}RECODE of {c |}
       gndr {c |}
(GESCHLECHT {c |}
          , {c |}
BEFRAGTE<R> {c |}
          ) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        158       53.20       53.20
{txt}     Female {c |}{res}        139       46.80      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        297      100.00
{txt}
{com}. sum age

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        297    53.15152    18.13968         18         89
{txt}
{com}. 
. tab treatment1 if st_terrori!=. & st_ethno!=. & sex!=. & age!=. & proedu2!=. & work2!=.

 {txt}treatment1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
    Control {c |}{res}        202       79.53       79.53
{txt}  Treatment {c |}{res}         52       20.47      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        254      100.00
{txt}
{com}. 
. 
. 
{txt}end of do-file

{com}. do "Appendix_C.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. 
. *-------------------------------------------------------------------------------
. *Appendix C: The salience of terrorism and public opinion in the context of 
. *the July 2016 terrorist attacks
. *-------------------------------------------------------------------------------
. 
. 
.                                          ***Overview of media analysis***
. 
. /*In order to understand the salience of terrorism surrounding the July 2016 
> terrorists attacks we conducted a media analysis. We analysed the salience of 
> terrorism throughout the GGSS data collection period, by searching for the 
> frequency of online and offline news articles containing the word "terror"
> - or the German variations of this word - in popular German news outlets. 
> Specifically, we searched for the term "terror*" (the * capturing varations of
> "terror", including the German equivalents of the words terrorist, terrorists, 
> terrorism, terror's, terrorist's and terrorists') restricting the time period
> to 01.03.2016-30.09.2016 (overlapping with the GGSS data collection period). 
> 
> To identify the number of daily articles refering to terrorism in the
> following news outlets we used NexisLexis: 
> 
>         - Bild, Bild.de
>         - Die Tageszeitung (TAZ) 
>         - Die Zeit, Zeit Magazin, Zeit Online
>  
>  The same procedure was repeated for the following news outlets, where we 
>  searched the news outlets' online archives for offline and online articles 
>  containing references to terrorism:
> 
>         - Frankfurter Allgemeine Zeitung (FAZ) (accessed via: https://fazarchiv.faz.net/)
>         - Süddeutsche Zeitung (SZ), Süddeutsche.de, SZ Magazin (accessed via: https://www.sz-archiv.de/)
> 
>         
> For each group of publications - those found via NexisLexis, those related to
> FAZ and those related to SZ - we noted the number of articles per day containing
> references to terrorism in an excel spreadsheet. This dataset forms the basis 
> for the following salience analysis of terrorism during the GGSS data collection
> period.  
> */
. 
. *-------------------------------------------------------------------------------
. 
.                                                         ***Media analysis***
.                                                         
.                                                                 *Figure C1*
.                                                         
. import excel "C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission\Media_data_version_09-02-2021.xlsx", sheet("Ark1") firstrow
{res}{txt}
{com}. 
. 
. gen terrorcount_all = t_FAZ + t_SZ + t_TAZ_Bild_Zeit
{txt}
{com}. 
. 
. *Timing of high-profile terrorist attacks during the fieldwork period 
. gen reflines=.
{txt}(214 missing values generated)

{com}. replace reflines = 140 if edate==td(22/03/2016) /*Brussels*/
{txt}(1 real change made)

{com}. replace reflines = 140 if edate==td(12/06/2016) /*Orlando*/
{txt}(1 real change made)

{com}. replace reflines = 140 if edate==td(14/07/2016) /*Nice*/
{txt}(1 real change made)

{com}. replace reflines = 140 if edate==td(18/07/2016) /*Würzburg*/
{txt}(1 real change made)

{com}. replace reflines = 140 if edate==td(22/07/2016) /*Munich*/
{txt}(1 real change made)

{com}. replace reflines = 140 if edate==td(24/07/2016) /*Ansbach*/
{txt}(1 real change made)

{com}. replace reflines = 140 if edate==td(26/07/2016) /*Normandy*/
{txt}(1 real change made)

{com}. 
. 
. *Mean number of articles in entire period
. sum terrorcount_all

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
terrorcoun~l {c |}{res}        214    44.61215    20.16956          5        127
{txt}
{com}. *Mean number of articles 44.61, rounded up 45 articles.
. 
. gen meanarticles =.
{txt}(214 missing values generated)

{com}. replace meanarticles = 45 if terrorcount_all !=.
{txt}(214 real changes made)

{com}. 
. 
. twoway (line meanarticles edate, lcolor(gs4) lpattern(vshortdash)) ///
>         (spike reflines edate, lcolor(gs6)) (line terrorcount_all edate, lcolor(black)), ///
>         ytitle(Number of articles per day (n)) ytitle(, size(medsmall)) ///
>         ylabel(0(20)140, labsize(medsmall) angle(horizontal) grid) xtitle(Date) ///
>         xtitle(, size(medsmall)) xlabel(, angle(stdarrow)) ///
>         legend(order(3 "Number of articles per day with reference to terrorism" ///
>         1 "Mean number of articles in entire period") cols(1) region(lwidth(none))) ///
>         xsize(7) plotregion(margin(zero))
{res}{txt}
{com}. 
. *Labels signifying location of terrorist attack is added manually added in the
. *graph editor.
. 
. 
. *After Brussels attack
. sum terrorcount_all if edate>=td(23/03/2016) & edate<=td(05/04/2016)

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
terrorcoun~l {c |}{res}         14    59.35714    33.54814         13        127
{txt}
{com}. 
. *Orlando
. sum terrorcount_all if edate>=td(13/06/2016) & edate<=td(26/06/2016)

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
terrorcoun~l {c |}{res}         14    45.35714    16.61143         17         69
{txt}
{com}. 
. *July terrorist attacks
. *First week after Nice attack
. sum terrorcount_all if edate>=td(15/07/2016) & edate<=td(28/07/2016)

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
terrorcoun~l {c |}{res}         14    76.64286    19.56688         36        100
{txt}
{com}. *Second week after Nice attack
. sum terrorcount_all if edate>=td(29/07/2016) & edate<=td(11/08/2016)

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
terrorcoun~l {c |}{res}         14    62.14286    17.61493         28         88
{txt}
{com}. 
. *First 4 weeks after Nice truck attack
. sum terrorcount_all if edate>=td(15/07/2016) & edate<=td(11/08/2016)

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
terrorcoun~l {c |}{res}         28    69.39286    19.70399         28        100
{txt}
{com}. *Level in August
. sum terrorcount_all if edate>=td(01/08/2016) & edate<=td(31/08/2016)

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
terrorcoun~l {c |}{res}         31    52.74194    15.57769         17         80
{txt}
{com}. 
. *-------------------------------------------------------------------------------
. 
. 
. 
{txt}end of do-file

{com}. do "Appendix_E.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. 
. *-------------------------------------------------------------------------------
. *Appendix E: List of variables
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. 
. ********************************************************************************
. 
. 
.                                                                 ***Table E1***
. 
. /*Means and standard deviations in table E1 are calculated for the studied sample
> meaning those respondents in the pre- and post-attacks group with non-missing 
> values on the relevant variables.*/
. 
. 
.                 *(1)Based on sample of respondents included in Models 1-3, Table 1*
. 
. *Dependent variables
. alphawgt  st_terror1 st_terror2 st_terror3 [aweight = wghtpew] if st_terrori!=. ///
>         & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. ///
>         & german==1, casewise item

{txt}Test scale = mean(unstandardized items)
Weights: aweight = wghtpew
                                                            average
                             item-test     item-rest      inter-item
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
st_terror1{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.8350{col 45} 0.6004{col 59} .0608668{col 73} 0.8112
{txt}st_terror2{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.8546{col 45} 0.6858{col 59} .0580719{col 73} 0.7195
{txt}st_terror3{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.8723{col 45} 0.7062{col 59} .0524448{col 73} 0.6939
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0571279{col 73} 0.8109
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. 
. svy: mean st_terrori if st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 2}st_terrori {c |}{col 14}{res}{space 2} .5546364{col 26}{space 2} .0196144{col 37}{space 5} .5157877{col 51}{space 3} .5934852
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 2}st_terrori {c |}{col 14}{result}{space 2} .5546364{col 27}{space 2} .2654207
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_terror1 if st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       259
{txt}{col 1}Number of PSUs{col 18}= {res}    119{txt}{col 35}Population size{col 51}={res} 257.218702
{txt}{col 35}Design df{col 51}= {res}       118

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 2}st_terror1 {c |}{col 14}{res}{space 2} .4283522{col 26}{space 2} .0227904{col 37}{space 5}  .383221{col 51}{space 3} .4734834
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 2}st_terror1 {c |}{col 14}{result}{space 2} .4283522{col 27}{space 2} .3348889
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_terror2 if st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       263
{txt}{col 1}Number of PSUs{col 18}= {res}    119{txt}{col 35}Population size{col 51}={res} 260.052578
{txt}{col 35}Design df{col 51}= {res}       118

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 2}st_terror2 {c |}{col 14}{res}{space 2} .6819211{col 26}{space 2} .0201491{col 37}{space 5} .6420203{col 51}{space 3} .7218218
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 2}st_terror2 {c |}{col 14}{result}{space 2} .6819211{col 27}{space 2} .2867289
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_terror3 if st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       261
{txt}{col 1}Number of PSUs{col 18}= {res}    118{txt}{col 35}Population size{col 51}={res} 257.584925
{txt}{col 35}Design df{col 51}= {res}       117

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 2}st_terror3 {c |}{col 14}{res}{space 2}  .553695{col 26}{space 2} .0229693{col 37}{space 5} .5082056{col 51}{space 3} .5991844
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 2}st_terror3 {c |}{col 14}{result}{space 2}  .553695{col 27}{space 2} .3034825
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. 
. *Ethnocentrism
. alphawgt st_foreign_advantage st_homocult st_imrights st_wlife st_immarry ///
>         st_stranger st_imenrich st_imcoherence st_imsocial st_diversity ///
>         [aweight = wghtpew] if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. ///
>         & proedu2!=. & work2!=. & treatment1!=. & german==1, casewise item

{txt}Test scale = mean(unstandardized items)
Weights: aweight = wghtpew
                                                            average
                             item-test     item-rest      inter-item
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
st_foreign~e{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.7412{col 45} 0.6773{col 59} .0320712{col 73} 0.8279
{txt}st_homocult{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.6370{col 45} 0.5310{col 59} .0322504{col 73} 0.8377
{txt}st_imrights{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.6881{col 45} 0.5841{col 59} .0309662{col 73} 0.8329
{txt}st_wlife{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.5577{col 45} 0.4619{col 59} .0343613{col 73} 0.8433
{txt}st_immarry{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.5688{col 45} 0.4675{col 59} .0339395{col 73} 0.8428
{txt}st_stranger{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.7403{col 45} 0.6385{col 59} .0293779{col 73} 0.8276
{txt}st_imenrich{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.7253{col 45} 0.6408{col 59} .0309754{col 73} 0.8280
{txt}st_imcoher~e{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.6759{col 45} 0.5764{col 59} .0315475{col 73} 0.8336
{txt}st_imsocial{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.6240{col 45} 0.4922{col 59} .0316139{col 73} 0.8432
{txt}st_diversity{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.5934{col 45} 0.4855{col 59} .0331827{col 73} 0.8415
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0320286{col 73} 0.8500
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. svy: mean st_ethno if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}st_ethno {c |}{col 14}{res}{space 2} .3880584{col 26}{space 2} .0130755{col 37}{space 5} .3621608{col 51}{space 3}  .413956
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 4}st_ethno {c |}{col 14}{result}{space 2} .3880584{col 27}{space 2} .1941183
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_diversity if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
st_diversity {c |}{col 14}{res}{space 2}  .344579{col 26}{space 2} .0194375{col 37}{space 5} .3060806{col 51}{space 3} .3830774
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
st_diversity {c |}{col 14}{result}{space 2}  .344579{col 27}{space 2} .2843529
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_foreign_advantage if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 22}{c |}{col 34}  Linearized
{col 22}{c |}       Mean{col 34}   Std. Err.{col 46}     [95% Con{col 59}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
st_foreign_advantage {c |}{col 22}{res}{space 2} .4499893{col 34}{space 2} .0148519{col 45}{space 5} .4205733{col 59}{space 3} .4794052
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
st_foreign~e {c |}{col 14}{result}{space 2} .4499893{col 27}{space 2}  .238978
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_homocult if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 1}st_homocult {c |}{col 14}{res}{space 2} .3428462{col 26}{space 2} .0216439{col 37}{space 5} .2999777{col 51}{space 3} .3857147
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 1}st_homocult {c |}{col 14}{result}{space 2} .3428462{col 27}{space 2}   .29892
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_imenrich if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 1}st_imenrich {c |}{col 14}{res}{space 2} .4509642{col 26}{space 2} .0179217{col 37}{space 5} .4154681{col 51}{space 3} .4864603
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 1}st_imenrich {c |}{col 14}{result}{space 2} .4509642{col 27}{space 2} .2923907
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_imsocial if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 1}st_imsocial {c |}{col 14}{res}{space 2} .4333663{col 26}{space 2} .0225287{col 37}{space 5} .3887455{col 51}{space 3} .4779872
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 1}st_imsocial {c |}{col 14}{result}{space 2} .4333663{col 27}{space 2} .3534354
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_imcoherence if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 16}{c |}       Mean{col 28}   Std. Err.{col 40}     [95% Con{col 53}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
st_imcoherence {c |}{col 16}{res}{space 2} .3291644{col 28}{space 2} .0206362{col 39}{space 5} .2882919{col 53}{space 3} .3700369
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
st_imcoher~e {c |}{col 14}{result}{space 2} .3291644{col 27}{space 2} .3028058
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_immarry if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 2}st_immarry {c |}{col 14}{res}{space 2} .1275924{col 26}{space 2} .0155851{col 37}{space 5} .0967242{col 51}{space 3} .1584606
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 2}st_immarry {c |}{col 14}{result}{space 2} .1275924{col 27}{space 2} .2599455
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_imrights if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 1}st_imrights {c |}{col 14}{res}{space 2} .2868658{col 26}{space 2} .0204107{col 37}{space 5} .2464398{col 51}{space 3} .3272919
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 1}st_imrights {c |}{col 14}{result}{space 2} .2868658{col 27}{space 2} .3221449
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_stranger if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 1}st_stranger {c |}{col 14}{res}{space 2}   .35768{col 26}{space 2}  .024101{col 37}{space 5} .3099449{col 51}{space 3}  .405415
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 1}st_stranger {c |}{col 14}{result}{space 2}   .35768{col 27}{space 2}   .35436
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_wlife if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}st_wlife {c |}{col 14}{res}{space 2}  .757536{col 26}{space 2} .0159222{col 37}{space 5} .7260002{col 51}{space 3} .7890719
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 4}st_wlife {c |}{col 14}{result}{space 2}  .757536{col 27}{space 2} .2434646
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. 
. *Post-attacks
. tab treatment1 if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1

 {txt}treatment1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
    Control {c |}{res}        202       79.53       79.53
{txt}  Treatment {c |}{res}         52       20.47      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        254      100.00
{txt}
{com}. 
. *Sociodemographic variables
. tab sex if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1

  {txt}RECODE of {c |}
       gndr {c |}
(GESCHLECHT {c |}
          , {c |}
BEFRAGTE<R> {c |}
          ) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        141       55.51       55.51
{txt}     Female {c |}{res}        113       44.49      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        254      100.00
{txt}
{com}. svy: mean age if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2} 53.47365{col 26}{space 2} 1.233664{col 37}{space 5} 51.03022{col 51}{space 3} 55.91708
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 9}age {c |}{col 14}{result}{space 2} 53.47365{col 27}{space 2} 18.41916
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. tab proedu2 if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1

       {txt}Highest level of education {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
          Lower secondary or less {c |}{res}         19        7.48        7.48
{txt}                  Upper secondary {c |}{res}        116       45.67       53.15
{txt}         Short tertiary education {c |}{res}         53       20.87       74.02
{txt}Medium to long tertiary education {c |}{res}         66       25.98      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}        254      100.00
{txt}
{com}. tab work2 if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1

      {txt}Working status {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
          Unemployed {c |}{res}         13        5.12        5.12
{txt}             Working {c |}{res}        130       51.18       56.30
{txt}             Retired {c |}{res}         87       34.25       90.55
{txt}           Housework {c |}{res}          9        3.54       94.09
{txt}In school or student {c |}{res}         15        5.91      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}        254      100.00
{txt}
{com}. 
. 
. 
.                 *(2)Based on sample of respondents included in Models 4-6, Table 1*
.                 
. *Alternative dependent variables
. alphawgt st_video st_email st_spyDE st_spyother [aweight = wghtpew] ///
>         if st_surveillanceindex!=. & st_ethno!=. & sex!=. & st_age!=. ///
>         & proedu2!=. & work2!=. & treatment1!=. & german==1, casewise item 

{txt}Test scale = mean(unstandardized items)
Weights: aweight = wghtpew
                                                            average
                             item-test     item-rest      inter-item
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
st_video{col 14}{c |}{res}{col 16} 249{col 24}+{col 31} 0.7431{col 45} 0.5176{col 59} .0455192{col 73} 0.7639
{txt}st_email{col 14}{c |}{res}{col 16} 249{col 24}+{col 31} 0.7679{col 45} 0.5576{col 59} .0430899{col 73} 0.7427
{txt}st_spyDE{col 14}{c |}{res}{col 16} 249{col 24}+{col 31} 0.8422{col 45} 0.7052{col 59} .0375829{col 73} 0.6674
{txt}st_spyother{col 14}{c |}{res}{col 16} 249{col 24}+{col 31} 0.7615{col 45} 0.5764{col 59} .0448756{col 73} 0.7323
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0427669{col 73} 0.7805
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. svy: mean st_surveillanceindex if st_surveillanceindex!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       249
{txt}{col 1}Number of PSUs{col 18}= {res}    116{txt}{col 35}Population size{col 51}={res} 245.580911
{txt}{col 35}Design df{col 51}= {res}       115

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 22}{c |}{col 34}  Linearized
{col 22}{c |}       Mean{col 34}   Std. Err.{col 46}     [95% Con{col 59}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
st_surveillanceindex {c |}{col 22}{res}{space 2} .4098628{col 34}{space 2} .0161916{col 45}{space 5} .3777904{col 59}{space 3} .4419352
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
st_surveil~x {c |}{col 14}{result}{space 2} .4098628{col 27}{space 2} .2340746
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_video if st_surveillanceindex!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       249
{txt}{col 1}Number of PSUs{col 18}= {res}    116{txt}{col 35}Population size{col 51}={res} 245.580911
{txt}{col 35}Design df{col 51}= {res}       115

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}st_video {c |}{col 14}{res}{space 2} .5855135{col 26}{space 2} .0217332{col 37}{space 5} .5424643{col 51}{space 3} .6285627
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 4}st_video {c |}{col 14}{result}{space 2} .5855135{col 27}{space 2} .3166733
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_email if st_surveillanceindex!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       249
{txt}{col 1}Number of PSUs{col 18}= {res}    116{txt}{col 35}Population size{col 51}={res} 245.580911
{txt}{col 35}Design df{col 51}= {res}       115

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}st_email {c |}{col 14}{res}{space 2} .3839749{col 26}{space 2} .0222725{col 37}{space 5} .3398574{col 51}{space 3} .4280924
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 4}st_email {c |}{col 14}{result}{space 2} .3839749{col 27}{space 2} .3160136
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_spyDE if st_surveillanceindex!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       249
{txt}{col 1}Number of PSUs{col 18}= {res}    116{txt}{col 35}Population size{col 51}={res} 245.580911
{txt}{col 35}Design df{col 51}= {res}       115

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}st_spyDE {c |}{col 14}{res}{space 2} .3286229{col 26}{space 2} .0197833{col 37}{space 5}  .289436{col 51}{space 3} .3678097
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 4}st_spyDE {c |}{col 14}{result}{space 2} .3286229{col 27}{space 2}  .286541
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_spyother if st_surveillanceindex!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       249
{txt}{col 1}Number of PSUs{col 18}= {res}    116{txt}{col 35}Population size{col 51}={res} 245.580911
{txt}{col 35}Design df{col 51}= {res}       115

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 1}st_spyother {c |}{col 14}{res}{space 2}   .34134{col 26}{space 2} .0196337{col 37}{space 5} .3024495{col 51}{space 3} .3802305
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 1}st_spyother {c |}{col 14}{result}{space 2}   .34134{col 27}{space 2} .2849552
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. 
. 
. 
.                 *(3)Based on sample of respondents included in Model 10, Table 1*
. 
. *Alternative independent variables
. svy: mean st_authorchild if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       254
{txt}{col 1}Number of PSUs{col 18}= {res}    117{txt}{col 35}Population size{col 51}={res} 251.049568
{txt}{col 35}Design df{col 51}= {res}       116

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 16}{c |}       Mean{col 28}   Std. Err.{col 40}     [95% Con{col 53}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
st_authorchild {c |}{col 16}{res}{space 2} .1766282{col 28}{space 2} .0161919{col 39}{space 5}  .144558{col 53}{space 3} .2086984
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
st_authorc~d {c |}{col 14}{result}{space 2} .1766282{col 27}{space 2} .2443027
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. 
. 
.         /*(4)Based on respondents with non-missing values on treatment variable,
>                 ethnocentrism index, and sociodemographic control variables*/
. 
. svy: mean st_lrID if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       248
{txt}{col 1}Number of PSUs{col 18}= {res}    116{txt}{col 35}Population size{col 51}={res} 244.347084
{txt}{col 35}Design df{col 51}= {res}       115

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 5}st_lrID {c |}{col 14}{res}{space 2}  .465627{col 26}{space 2} .0118971{col 37}{space 5} .4420612{col 51}{space 3} .4891929
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 5}st_lrID {c |}{col 14}{result}{space 2}  .465627{col 27}{space 2} .1795403
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. 
. 
.         /*(5)Based on respondents with non-missing values on alternative control variables,
>                 ethnocentrism index, and sociodemographic control variables*/
. 
. tab control_1 if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1

  {txt}control_1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        456       89.76       89.76
{txt}          1 {c |}{res}         52       10.24      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        508      100.00
{txt}
{com}. tab control_2 if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1

  {txt}control_2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        240       82.19       82.19
{txt}          1 {c |}{res}         52       17.81      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        292      100.00
{txt}
{com}. tab control_3 if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1

  {txt}control_3 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        109       67.70       67.70
{txt}          1 {c |}{res}         52       32.30      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        161      100.00
{txt}
{com}. 
. 
.                 *(1)Based on sample of respondents included in Models 1-3, Table 1*
. 
. 
. *Contact variables
. svy: mean st_phone if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}     1,381
{txt}{col 1}Number of PSUs{col 18}= {res}    162{txt}{col 35}Population size{col 51}={res} 1,355.7778
{txt}{col 35}Design df{col 51}= {res}       161

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}st_phone {c |}{col 14}{res}{space 2} .0416666{col 26}{space 2} .0037449{col 37}{space 5} .0342711{col 51}{space 3}  .049062
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 4}st_phone {c |}{col 14}{result}{space 2} .0416666{col 27}{space 2} .0859815
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_housevisits if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}     1,381
{txt}{col 1}Number of PSUs{col 18}= {res}    162{txt}{col 35}Population size{col 51}={res} 1,355.7778
{txt}{col 35}Design df{col 51}= {res}       161

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}{col 28}  Linearized
{col 16}{c |}       Mean{col 28}   Std. Err.{col 40}     [95% Con{col 53}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
st_housevisits {c |}{col 16}{res}{space 2} .0684158{col 28}{space 2} .0035139{col 39}{space 5} .0614765{col 53}{space 3}  .075355
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
st_housevi~s {c |}{col 14}{result}{space 2} .0684158{col 27}{space 2} .1004801
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_othercontacts if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}     1,381
{txt}{col 1}Number of PSUs{col 18}= {res}    162{txt}{col 35}Population size{col 51}={res} 1,355.7778
{txt}{col 35}Design df{col 51}= {res}       161

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 18}{c |}{col 30}  Linearized
{col 18}{c |}       Mean{col 30}   Std. Err.{col 42}     [95% Con{col 55}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
st_othercontacts {c |}{col 18}{res}{space 2} .0068372{col 30}{space 2} .0015829{col 41}{space 5} .0037113{col 55}{space 3}  .009963
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
st_otherco~s {c |}{col 14}{result}{space 2} .0068372{col 27}{space 2} .0496344
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. 
. *Bavaria dummy
. tab statedummy if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1

 {txt}statedummy {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
Not Bavaria {c |}{res}      1,217       88.12       88.12
{txt}    Bavaria {c |}{res}        164       11.88      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,381      100.00
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. 
{txt}end of do-file

{com}. do "Appendix_F.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. 
. *-------------------------------------------------------------------------------
. *Appendix F: Conceptualizing and operationalizing ethnocentrism
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. 
. 
.                                 
. ********************************************************************************
. *F2 Operationalization and delineation from other related concepts
. ********************************************************************************
. 
. 
. *Statistics for whole German sample
. spearman st_foreign_advantage st_homocult st_imrights st_wlife st_immarry ///
> st_stranger st_imenrich st_imcoherence st_imsocial st_diversity if st_terrori!=. ///
>         & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. ///
>         & german==1, stats(rho obs p)

{txt}{c TLC }{hline 17}{c TRC}
{c |}  Key{col 19}{c |}
{c LT }{hline 17}{c RT}
{c |}  {it: rho}{col 19}{c |}
{c |}  {it: Number of obs}{col 19}{c |}
{c |}  {it: Sig. level}{col 19}{c |}
{c BLC }{hline 17}{c BRC}

             {c |} st_for~e st_hom~t st_imr~s st_wlife st_imm~y st_str~r st_ime~h st_imc~e st_ims~l st_div~y
{hline 13}{c +}{hline 90}
st_foreign~e {c |} {res}  1.0000 
             {txt}{c |} {res}     254 
             {txt}{c |} 
             {c |}
 st_homocult {c |} {res}  0.3452   1.0000 
             {txt}{c |} {res}     254      254 
             {txt}{c |} {res}  0.0000 
             {txt}{c |}
 st_imrights {c |} {res}  0.4635   0.3223   1.0000 
             {txt}{c |} {res}     254      254      254 
             {txt}{c |} {res}  0.0000   0.0000 
             {txt}{c |}
    st_wlife {c |} {res}  0.3529   0.2490   0.3788   1.0000 
             {txt}{c |} {res}     254      254      254      254 
             {txt}{c |} {res}  0.0000   0.0001   0.0000 
             {txt}{c |}
  st_immarry {c |} {res}  0.3446   0.3426   0.4867   0.2674   1.0000 
             {txt}{c |} {res}     254      254      254      254      254 
             {txt}{c |} {res}  0.0000   0.0000   0.0000   0.0000 
             {txt}{c |}
 st_stranger {c |} {res}  0.6135   0.3194   0.4421   0.4655   0.4023   1.0000 
             {txt}{c |} {res}     254      254      254      254      254      254 
             {txt}{c |} {res}  0.0000   0.0000   0.0000   0.0000   0.0000 
             {txt}{c |}
 st_imenrich {c |} {res}  0.5281   0.3639   0.3695   0.3591   0.4389   0.4574   1.0000 
             {txt}{c |} {res}     254      254      254      254      254      254      254 
             {txt}{c |} {res}  0.0000   0.0000   0.0000   0.0000   0.0000   0.0000 
             {txt}{c |}
st_imcoher~e {c |} {res}  0.4258   0.3343   0.4334   0.3208   0.3920   0.5117   0.4295   1.0000 
             {txt}{c |} {res}     254      254      254      254      254      254      254      254 
             {txt}{c |} {res}  0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000 
             {txt}{c |}
 st_imsocial {c |} {res}  0.4952   0.2630   0.3085   0.3229   0.2269   0.3765   0.5036   0.3085   1.0000 
             {txt}{c |} {res}     254      254      254      254      254      254      254      254      254 
             {txt}{c |} {res}  0.0000   0.0000   0.0000   0.0000   0.0003   0.0000   0.0000   0.0000 
             {txt}{c |}
st_diversity {c |} {res}  0.3686   0.4418   0.4013   0.1917   0.2990   0.3620   0.3752   0.2874   0.2800   1.0000 
             {txt}{c |} {res}     254      254      254      254      254      254      254      254      254      254 
             {txt}{c |} {res}  0.0000   0.0000   0.0000   0.0021   0.0000   0.0000   0.0000   0.0000   0.0000 
             {txt}{c |}

{com}. factor st_foreign_advantage st_homocult st_imrights st_wlife st_immarry ///
> st_stranger st_imenrich st_imcoherence st_imsocial st_diversity [aweight = wghtpew] ///
>         if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. ///
>         & work2!=. & treatment1!=. & german==1, pcf
{txt}(sum of wgt is   2.5105e+02)
(obs=254)

Factor analysis/correlation{col 50}Number of obs    = {res}       254
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       2
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      19

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      4.34489      3.32373            0.4345       0.4345
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.02116      0.14860            0.1021       0.5366
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.87256      0.11210            0.0873       0.6239
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.76046      0.09089            0.0760       0.6999
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.66958      0.04173            0.0670       0.7669
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.62784      0.13192            0.0628       0.8297
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}      0.49592      0.03091            0.0496       0.8792
{txt}{col 5}{ralign 11:Factor8}  {c |}{res}      0.46501      0.05912            0.0465       0.9257
{txt}{col 5}{ralign 11:Factor9}  {c |}{res}      0.40589      0.06923            0.0406       0.9663
{txt}{col 5}{ralign 11:Factor10}  {c |}{res}      0.33667            .            0.0337       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}45{txt}) ={res}  810.49{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:st_foreign~e}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7648}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2440}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3555}}}{space 1}
{space 4}{space 0}{ralign 12:st_homocult}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6330}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2969}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5111}}}{space 1}
{space 4}{space 0}{ralign 12:st_imrights}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6836}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3056}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4393}}}{space 1}
{space 4}{space 0}{ralign 12:st_wlife}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5595}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3159}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5872}}}{space 1}
{space 4}{space 0}{ralign 12:st_immarry}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5685}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4945}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4323}}}{space 1}
{space 4}{space 0}{ralign 12:st_stranger}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7403}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1681}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4237}}}{space 1}
{space 4}{space 0}{ralign 12:st_imenrich}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7345}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1735}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4305}}}{space 1}
{space 4}{space 0}{ralign 12:st_imcoher~e}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6778}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1131}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5278}}}{space 1}
{space 4}{space 0}{ralign 12:st_imsocial}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6051}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.5228}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3606}}}{space 1}
{space 4}{space 0}{ralign 12:st_diversity}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5854}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3021}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5660}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. rotate

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}       254
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       2
{col 5}{txt}Rotation: orthogonal varimax (Kaiser off){col 50}Number of params =   {res}      19

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Variance}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.82592      0.28577            0.2826       0.2826
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      2.54014            .            0.2540       0.5366
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}45{txt}) ={res}  810.49{txt} Prob>chi2 ={res} 0.0000

{txt}Rotated factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:st_foreign~e}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7285}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3372}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3555}}}{space 1}
{space 4}{space 0}{ralign 12:st_homocult}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2657}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6467}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5111}}}{space 1}
{space 4}{space 0}{ralign 12:st_imrights}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2971}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6874}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4393}}}{space 1}
{space 4}{space 0}{ralign 12:st_wlife}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6258}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1455}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5872}}}{space 1}
{space 4}{space 0}{ralign 12:st_immarry}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0846}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.7487}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4323}}}{space 1}
{space 4}{space 0}{ralign 12:st_stranger}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6592}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3766}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4237}}}{space 1}
{space 4}{space 0}{ralign 12:st_imenrich}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6585}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3687}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4305}}}{space 1}
{space 4}{space 0}{ralign 12:st_imcoher~e}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4229}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5415}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5278}}}{space 1}
{space 4}{space 0}{ralign 12:st_imsocial}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7993}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0238}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3606}}}{space 1}
{space 4}{space 0}{ralign 12:st_diversity}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2272}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6184}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5660}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

Factor rotation matrix

{space 4}{hline 13}{c  TT}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 7:Factor1}{space 1}{space 1}{ralign 7:Factor2}{space 1}
{space 4}{hline 13}{c   +}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:Factor1}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.7369}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.6760}}}{space 1}
{space 4}{space 0}{ralign 12:Factor2}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.6760}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.7369}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 9}{hline 9}

{com}. 
. alphawgt st_foreign_advantage st_homocult st_imrights st_wlife st_immarry ///
>         st_stranger st_imenrich st_imcoherence st_imsocial st_diversity ///
>         [aweight = wghtpew] if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. ///
>         & proedu2!=. & work2!=. & treatment1!=. & german==1, casewise item

{txt}Test scale = mean(unstandardized items)
Weights: aweight = wghtpew
                                                            average
                             item-test     item-rest      inter-item
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
st_foreign~e{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.7412{col 45} 0.6773{col 59} .0320712{col 73} 0.8279
{txt}st_homocult{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.6370{col 45} 0.5310{col 59} .0322504{col 73} 0.8377
{txt}st_imrights{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.6881{col 45} 0.5841{col 59} .0309662{col 73} 0.8329
{txt}st_wlife{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.5577{col 45} 0.4619{col 59} .0343613{col 73} 0.8433
{txt}st_immarry{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.5688{col 45} 0.4675{col 59} .0339395{col 73} 0.8428
{txt}st_stranger{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.7403{col 45} 0.6385{col 59} .0293779{col 73} 0.8276
{txt}st_imenrich{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.7253{col 45} 0.6408{col 59} .0309754{col 73} 0.8280
{txt}st_imcoher~e{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.6759{col 45} 0.5764{col 59} .0315475{col 73} 0.8336
{txt}st_imsocial{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.6240{col 45} 0.4922{col 59} .0316139{col 73} 0.8432
{txt}st_diversity{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.5934{col 45} 0.4855{col 59} .0331827{col 73} 0.8415
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0320286{col 73} 0.8500
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. 
. 
. *Statistics for studied subsample
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. keep if german==1 & governmentsplit!=. & treatment1!=.                          
{txt}(3,193 observations deleted)

{com}. 
. 
. 
. *Analyzing if "Way of life" variable (difference in wording) changes the results
. *Inspecting means and standard deviations                       
. svy: mean st_wlife if st_split_a_WL!=. & st_terrori!=. & st_ethno!=. & sex!=. ///
>         & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       125
{txt}{col 1}Number of PSUs{col 18}= {res}     88{txt}{col 35}Population size{col 51}={res} 120.605461
{txt}{col 35}Design df{col 51}= {res}        87

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}st_wlife {c |}{col 14}{res}{space 2} .7703451{col 26}{space 2} .0217415{col 37}{space 5} .7271315{col 51}{space 3} .8135586
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 4}st_wlife {c |}{col 14}{result}{space 2} .7703451{col 27}{space 2} .2382852
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean st_wlife if st_split_b_WL!=. & st_terrori!=. & st_ethno!=. & sex!=. ///
>         & st_age!=. & proedu2!=. & work2!=. & treatment1!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       129
{txt}{col 1}Number of PSUs{col 18}= {res}     78{txt}{col 35}Population size{col 51}={res} 130.444106
{txt}{col 35}Design df{col 51}= {res}        77

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}st_wlife {c |}{col 14}{res}{space 2} .7456931{col 26}{space 2} .0239002{col 37}{space 5} .6981017{col 51}{space 3} .7932844
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 4}st_wlife {c |}{col 14}{result}{space 2} .7456931{col 27}{space 2} .2484743
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. 
.                         
. 
.  
. ********************************************************************************
. *F3: Addressing the difference in question working of one ethnocentrism indicator
. ********************************************************************************
. 
. 
.                                                                                 *Table F1*
. *Full ethnocentrism index
. *SPLIT A
. svy: regress st_terrori c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. & st_split_a_WL!=. & german==1
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       125
{txt}{col 1}Number of PSUs{col 20}= {res}       88{txt}{col 49}Population size{col 67}={res} 120.605461
{txt}{col 49}Design df{col 67}= {res}        87
{txt}{col 49}F({res}  11{txt},{res}     77{txt}){col 67}= {res}      1.44
{txt}{col 49}Prob > F{col 67}= {res}    0.1720
{txt}{col 49}R-squared{col 67}= {res}    0.0877

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1422647{col 48}{space 2} .1337119{col 59}{space 1}    1.06{col 68}{space 3}0.290{col 76}{space 4}-.1235022{col 89}{space 3} .4080316
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0638798{col 48}{space 2} .0560925{col 59}{space 1}    1.14{col 68}{space 3}0.258{col 76}{space 4}-.0476102{col 89}{space 3} .1753698
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4111201{col 48}{space 2} .4603792{col 59}{space 1}    0.89{col 68}{space 3}0.374{col 76}{space 4}-.5039334{col 89}{space 3} 1.326174
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.3370627{col 48}{space 2} .4811195{col 59}{space 1}   -0.70{col 68}{space 3}0.485{col 76}{space 4} -1.29334{col 89}{space 3} .6192144
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1862893{col 48}{space 2} .0744753{col 59}{space 1}   -2.50{col 68}{space 3}0.014{col 76}{space 4} -.334317{col 89}{space 3}-.0382616
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} -.112688{col 48}{space 2} .0811774{col 59}{space 1}   -1.39{col 68}{space 3}0.169{col 76}{space 4} -.274037{col 89}{space 3} .0486609
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1669029{col 48}{space 2} .0897622{col 59}{space 1}   -1.86{col 68}{space 3}0.066{col 76}{space 4}-.3453149{col 89}{space 3} .0115092
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0193432{col 48}{space 2} .0931905{col 59}{space 1}    0.21{col 68}{space 3}0.836{col 76}{space 4}-.1658831{col 89}{space 3} .2045695
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0040282{col 48}{space 2} .1119081{col 59}{space 1}    0.04{col 68}{space 3}0.971{col 76}{space 4}-.2184013{col 89}{space 3} .2264576
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0418857{col 48}{space 2} .1537426{col 59}{space 1}   -0.27{col 68}{space 3}0.786{col 76}{space 4}-.3474658{col 89}{space 3} .2636944
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0652044{col 48}{space 2} .1410833{col 59}{space 1}    0.46{col 68}{space 3}0.645{col 76}{space 4} -.215214{col 89}{space 3} .3456229
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5365023{col 48}{space 2} .1671086{col 59}{space 1}    3.21{col 68}{space 3}0.002{col 76}{space 4} .2043559{col 89}{space 3} .8686488
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(*,**) alpha(0.05,0.01) label(proper) replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. svy: regress st_terrori c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if st_split_a_WL!=. & german==1
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       125
{txt}{col 1}Number of PSUs{col 20}= {res}       88{txt}{col 49}Population size{col 67}={res} 120.605461
{txt}{col 49}Design df{col 67}= {res}        87
{txt}{col 49}F({res}  12{txt},{res}     76{txt}){col 67}= {res}      1.86
{txt}{col 49}Prob > F{col 67}= {res}    0.0528
{txt}{col 49}R-squared{col 67}= {res}    0.1298

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1298889{col 48}{space 2} .1305407{col 59}{space 1}    1.00{col 68}{space 3}0.322{col 76}{space 4}-.1295749{col 89}{space 3} .3893527
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}  .138379{col 48}{space 2} .0624283{col 59}{space 1}    2.22{col 68}{space 3}0.029{col 76}{space 4}  .014296{col 89}{space 3}  .262462
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0461901{col 48}{space 2} .0564235{col 59}{space 1}    0.82{col 68}{space 3}0.415{col 76}{space 4}-.0659577{col 89}{space 3} .1583379
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4598802{col 48}{space 2} .4562075{col 59}{space 1}    1.01{col 68}{space 3}0.316{col 76}{space 4}-.4468815{col 89}{space 3} 1.366642
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.3843027{col 48}{space 2} .4681226{col 59}{space 1}   -0.82{col 68}{space 3}0.414{col 76}{space 4}-1.314747{col 89}{space 3} .5461416
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1791029{col 48}{space 2} .0690568{col 59}{space 1}   -2.59{col 68}{space 3}0.011{col 76}{space 4}-.3163607{col 89}{space 3}-.0418451
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1024537{col 48}{space 2} .0782698{col 59}{space 1}   -1.31{col 68}{space 3}0.194{col 76}{space 4}-.2580234{col 89}{space 3}  .053116
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1834944{col 48}{space 2} .0847297{col 59}{space 1}   -2.17{col 68}{space 3}0.033{col 76}{space 4}-.3519037{col 89}{space 3} -.015085
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0087005{col 48}{space 2} .0892885{col 59}{space 1}   -0.10{col 68}{space 3}0.923{col 76}{space 4}-.1861711{col 89}{space 3}   .16877
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0015682{col 48}{space 2}  .106453{col 59}{space 1}    0.01{col 68}{space 3}0.988{col 76}{space 4}-.2100188{col 89}{space 3} .2131551
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0182219{col 48}{space 2} .1497482{col 59}{space 1}   -0.12{col 68}{space 3}0.903{col 76}{space 4}-.3158628{col 89}{space 3} .2794189
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0576298{col 48}{space 2} .1370622{col 59}{space 1}    0.42{col 68}{space 3}0.675{col 76}{space 4}-.2147962{col 89}{space 3} .3300558
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5233588{col 48}{space 2} .1622291{col 59}{space 1}    3.23{col 68}{space 3}0.002{col 76}{space 4} .2009108{col 89}{space 3} .8458067
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(*,**) alpha(0.05,0.01) label(proper) append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if st_split_a_WL!=. & german==1
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       125
{txt}{col 1}Number of PSUs{col 20}= {res}       88{txt}{col 49}Population size{col 67}={res} 120.605461
{txt}{col 49}Design df{col 67}= {res}        87
{txt}{col 49}F({res}  13{txt},{res}     75{txt}){col 67}= {res}      3.75
{txt}{col 49}Prob > F{col 67}= {res}    0.0001
{txt}{col 49}R-squared{col 67}= {res}    0.1901

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2}-.0267198{col 48}{space 2}  .140198{col 59}{space 1}   -0.19{col 68}{space 3}0.849{col 76}{space 4}-.3053785{col 89}{space 3} .2519389
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1670737{col 48}{space 2} .0927976{col 59}{space 1}   -1.80{col 68}{space 3}0.075{col 76}{space 4} -.351519{col 89}{space 3} .0173716
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}  .777237{col 48}{space 2} .1959425{col 59}{space 1}    3.97{col 68}{space 3}0.000{col 76}{space 4} .3877802{col 89}{space 3} 1.166694
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0496055{col 48}{space 2} .0543635{col 59}{space 1}    0.91{col 68}{space 3}0.364{col 76}{space 4}-.0584478{col 89}{space 3} .1576588
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5871973{col 48}{space 2} .4449561{col 59}{space 1}    1.32{col 68}{space 3}0.190{col 76}{space 4}-.2972012{col 89}{space 3} 1.471596
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} -.514152{col 48}{space 2} .4505697{col 59}{space 1}   -1.14{col 68}{space 3}0.257{col 76}{space 4}-1.409708{col 89}{space 3} .3814039
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1386453{col 48}{space 2} .0673255{col 59}{space 1}   -2.06{col 68}{space 3}0.042{col 76}{space 4}-.2724619{col 89}{space 3}-.0048287
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0567891{col 48}{space 2} .0787898{col 59}{space 1}   -0.72{col 68}{space 3}0.473{col 76}{space 4}-.2133923{col 89}{space 3} .0998142
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1451775{col 48}{space 2} .0780928{col 59}{space 1}   -1.86{col 68}{space 3}0.066{col 76}{space 4}-.3003954{col 89}{space 3} .0100405
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0057756{col 48}{space 2} .0877841{col 59}{space 1}   -0.07{col 68}{space 3}0.948{col 76}{space 4}-.1802561{col 89}{space 3} .1687049
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0016712{col 48}{space 2} .1034267{col 59}{space 1}   -0.02{col 68}{space 3}0.987{col 76}{space 4}-.2072429{col 89}{space 3} .2039005
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0142969{col 48}{space 2} .1489258{col 59}{space 1}   -0.10{col 68}{space 3}0.924{col 76}{space 4}-.3103031{col 89}{space 3} .2817092
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0772012{col 48}{space 2} .1363126{col 59}{space 1}    0.57{col 68}{space 3}0.573{col 76}{space 4}-.1937348{col 89}{space 3} .3481372
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5214258{col 48}{space 2} .1578553{col 59}{space 1}    3.30{col 68}{space 3}0.001{col 76}{space 4} .2076713{col 89}{space 3} .8351803
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(*,**) alpha(0.05,0.01) label(proper) append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. *SPLIT B
. svy: regress st_terrori c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. & st_split_b_WL!=. & german==1
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       129
{txt}{col 1}Number of PSUs{col 20}= {res}       78{txt}{col 49}Population size{col 67}={res} 130.444106
{txt}{col 49}Design df{col 67}= {res}        77
{txt}{col 49}F({res}  11{txt},{res}     67{txt}){col 67}= {res}      1.28
{txt}{col 49}Prob > F{col 67}= {res}    0.2556
{txt}{col 49}R-squared{col 67}= {res}    0.1099

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1501362{col 48}{space 2} .1251516{col 59}{space 1}    1.20{col 68}{space 3}0.234{col 76}{space 4}-.0990725{col 89}{space 3} .3993449
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0537986{col 48}{space 2} .0519805{col 59}{space 1}   -1.03{col 68}{space 3}0.304{col 76}{space 4} -.157305{col 89}{space 3} .0497078
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7021056{col 48}{space 2} .4245477{col 59}{space 1}    1.65{col 68}{space 3}0.102{col 76}{space 4}-.1432769{col 89}{space 3} 1.547488
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6610853{col 48}{space 2} .5383135{col 59}{space 1}   -1.23{col 68}{space 3}0.223{col 76}{space 4}-1.733004{col 89}{space 3} .4108339
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0807066{col 48}{space 2} .1097084{col 59}{space 1}   -0.74{col 68}{space 3}0.464{col 76}{space 4} -.299164{col 89}{space 3} .1377508
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0354142{col 48}{space 2}  .112867{col 59}{space 1}   -0.31{col 68}{space 3}0.755{col 76}{space 4}-.2601612{col 89}{space 3} .1893327
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1063413{col 48}{space 2}    .1174{col 59}{space 1}   -0.91{col 68}{space 3}0.368{col 76}{space 4}-.3401146{col 89}{space 3}  .127432
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.1148735{col 48}{space 2} .1108983{col 59}{space 1}   -1.04{col 68}{space 3}0.304{col 76}{space 4}-.3357001{col 89}{space 3} .1059531
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1262114{col 48}{space 2} .1359638{col 59}{space 1}   -0.93{col 68}{space 3}0.356{col 76}{space 4}  -.39695{col 89}{space 3} .1445271
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0719658{col 48}{space 2} .1774808{col 59}{space 1}    0.41{col 68}{space 3}0.686{col 76}{space 4}-.2814436{col 89}{space 3} .4253751
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.2400555{col 48}{space 2} .1578016{col 59}{space 1}   -1.52{col 68}{space 3}0.132{col 76}{space 4}-.5542787{col 89}{space 3} .0741676
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5421123{col 48}{space 2}  .141181{col 59}{space 1}    3.84{col 68}{space 3}0.000{col 76}{space 4} .2609851{col 89}{space 3} .8232395
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(*,**) alpha(0.05,0.01) label(proper) append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. svy: regress st_terrori c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if st_split_b_WL!=. & german==1
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       129
{txt}{col 1}Number of PSUs{col 20}= {res}       78{txt}{col 49}Population size{col 67}={res} 130.444106
{txt}{col 49}Design df{col 67}= {res}        77
{txt}{col 49}F({res}  12{txt},{res}     66{txt}){col 67}= {res}      1.20
{txt}{col 49}Prob > F{col 67}= {res}    0.3042
{txt}{col 49}R-squared{col 67}= {res}    0.1222

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1469367{col 48}{space 2} .1260264{col 59}{space 1}    1.17{col 68}{space 3}0.247{col 76}{space 4}-.1040139{col 89}{space 3} .3978874
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .0796159{col 48}{space 2} .0495307{col 59}{space 1}    1.61{col 68}{space 3}0.112{col 76}{space 4}-.0190124{col 89}{space 3} .1782442
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0539543{col 48}{space 2} .0515717{col 59}{space 1}   -1.05{col 68}{space 3}0.299{col 76}{space 4}-.1566466{col 89}{space 3}  .048738
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6853011{col 48}{space 2} .4100225{col 59}{space 1}    1.67{col 68}{space 3}0.099{col 76}{space 4} -.131158{col 89}{space 3}  1.50176
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6748552{col 48}{space 2} .5205249{col 59}{space 1}   -1.30{col 68}{space 3}0.199{col 76}{space 4}-1.711353{col 89}{space 3} .3616422
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0712581{col 48}{space 2} .0986237{col 59}{space 1}   -0.72{col 68}{space 3}0.472{col 76}{space 4} -.267643{col 89}{space 3} .1251267
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0274878{col 48}{space 2} .1054208{col 59}{space 1}   -0.26{col 68}{space 3}0.795{col 76}{space 4}-.2374074{col 89}{space 3} .1824317
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1022575{col 48}{space 2} .1108516{col 59}{space 1}   -0.92{col 68}{space 3}0.359{col 76}{space 4}-.3229913{col 89}{space 3} .1184762
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.1178885{col 48}{space 2} .1188184{col 59}{space 1}   -0.99{col 68}{space 3}0.324{col 76}{space 4}-.3544862{col 89}{space 3} .1187091
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1223925{col 48}{space 2} .1435324{col 59}{space 1}   -0.85{col 68}{space 3}0.396{col 76}{space 4} -.408202{col 89}{space 3} .1634171
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}  .082605{col 48}{space 2} .1825811{col 59}{space 1}    0.45{col 68}{space 3}0.652{col 76}{space 4}-.2809603{col 89}{space 3} .4461704
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.2494044{col 48}{space 2} .1601956{col 59}{space 1}   -1.56{col 68}{space 3}0.124{col 76}{space 4}-.5683946{col 89}{space 3} .0695859
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5341296{col 48}{space 2} .1413311{col 59}{space 1}    3.78{col 68}{space 3}0.000{col 76}{space 4} .2527034{col 89}{space 3} .8155558
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(*,**) alpha(0.05,0.01) label(proper) append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if st_split_b_WL!=. & german==1
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       129
{txt}{col 1}Number of PSUs{col 20}= {res}       78{txt}{col 49}Population size{col 67}={res} 130.444106
{txt}{col 49}Design df{col 67}= {res}        77
{txt}{col 49}F({res}  13{txt},{res}     65{txt}){col 67}= {res}      2.65
{txt}{col 49}Prob > F{col 67}= {res}    0.0049
{txt}{col 49}R-squared{col 67}= {res}    0.1516

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0039864{col 48}{space 2} .1370821{col 59}{space 1}    0.03{col 68}{space 3}0.977{col 76}{space 4}-.2689789{col 89}{space 3} .2769518
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1677868{col 48}{space 2} .1088368{col 59}{space 1}   -1.54{col 68}{space 3}0.127{col 76}{space 4}-.3845086{col 89}{space 3}  .048935
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6294348{col 48}{space 2} .2409912{col 59}{space 1}    2.61{col 68}{space 3}0.011{col 76}{space 4} .1495601{col 89}{space 3}  1.10931
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0485391{col 48}{space 2}  .051821{col 59}{space 1}   -0.94{col 68}{space 3}0.352{col 76}{space 4}-.1517279{col 89}{space 3} .0546498
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7676193{col 48}{space 2} .3950804{col 59}{space 1}    1.94{col 68}{space 3}0.056{col 76}{space 4}-.0190863{col 89}{space 3} 1.554325
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} -.769808{col 48}{space 2} .5157387{col 59}{space 1}   -1.49{col 68}{space 3}0.140{col 76}{space 4}-1.796775{col 89}{space 3}  .257159
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0636922{col 48}{space 2} .0968744{col 59}{space 1}   -0.66{col 68}{space 3}0.513{col 76}{space 4}-.2565937{col 89}{space 3} .1292093
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0083128{col 48}{space 2} .1022994{col 59}{space 1}   -0.08{col 68}{space 3}0.935{col 76}{space 4} -.212017{col 89}{space 3} .1953914
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0778599{col 48}{space 2} .1086068{col 59}{space 1}   -0.72{col 68}{space 3}0.476{col 76}{space 4}-.2941236{col 89}{space 3} .1384039
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.1595857{col 48}{space 2} .0983157{col 59}{space 1}   -1.62{col 68}{space 3}0.109{col 76}{space 4}-.3553572{col 89}{space 3} .0361858
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} -.169305{col 48}{space 2} .1257922{col 59}{space 1}   -1.35{col 68}{space 3}0.182{col 76}{space 4}-.4197892{col 89}{space 3} .0811792
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}  .029553{col 48}{space 2} .1672548{col 59}{space 1}    0.18{col 68}{space 3}0.860{col 76}{space 4}-.3034939{col 89}{space 3} .3625999
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.2951617{col 48}{space 2} .1460361{col 59}{space 1}   -2.02{col 68}{space 3}0.047{col 76}{space 4}-.5859568{col 89}{space 3}-.0043667
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6000395{col 48}{space 2}  .132664{col 59}{space 1}    4.52{col 68}{space 3}0.000{col 76}{space 4} .3358717{col 89}{space 3} .8642072
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(*,**) alpha(0.05,0.01) label(proper) append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. 
. 
. 
{txt}end of do-file

{com}. do "Appendix_I.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. 
. 
. *-------------------------------------------------------------------------------
. *Appendix I: Construction of main and alternative dependent variable 
. *-------------------------------------------------------------------------------
. 
.                                         ***I1: Civil liberties (terrorism)***
. 
. *Factor analysis
. spearman st_terror1 st_terror2 st_terror3 if st_terrori!=. ///
>         & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. ///
>         & german==1, stats(rho obs p)

{txt}{c TLC }{hline 17}{c TRC}
{c |}  Key{col 19}{c |}
{c LT }{hline 17}{c RT}
{c |}  {it: rho}{col 19}{c |}
{c |}  {it: Number of obs}{col 19}{c |}
{c |}  {it: Sig. level}{col 19}{c |}
{c BLC }{hline 17}{c BRC}

             {c |} st_ter~1 st_ter~2 st_ter~3
{hline 13}{c +}{hline 27}
  st_terror1 {c |} {res}  1.0000 
             {txt}{c |} {res}     254 
             {txt}{c |} 
             {c |}
  st_terror2 {c |} {res}  0.5594   1.0000 
             {txt}{c |} {res}     254      254 
             {txt}{c |} {res}  0.0000 
             {txt}{c |}
  st_terror3 {c |} {res}  0.5636   0.6621   1.0000 
             {txt}{c |} {res}     254      254      254 
             {txt}{c |} {res}  0.0000   0.0000 
             {txt}{c |}

{com}. factor st_terror1 st_terror2 st_terror3 [aweight = wghtpew] ///
>         if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. ///
>         & work2!=. & treatment1!=. & german==1, pcf
{txt}(sum of wgt is   2.5105e+02)
(obs=254)

Factor analysis/correlation{col 50}Number of obs    = {res}       254
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       1
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.19198      1.69906            0.7307       0.7307
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.49293      0.17784            0.1643       0.8950
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.31509            .            0.1050       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res}  271.71{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:st_terror1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8097}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3443}}}{space 1}
{space 4}{space 0}{ralign 12:st_terror2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8705}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2422}}}{space 1}
{space 4}{space 0}{ralign 12:st_terror3}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8824}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2214}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. alphawgt st_terror1 st_terror2 st_terror3 [aweight = wghtpew] ///
>         if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. ///
>         & proedu2!=. & work2!=. & treatment1!=. & german==1, casewise item

{txt}Test scale = mean(unstandardized items)
Weights: aweight = wghtpew
                                                            average
                             item-test     item-rest      inter-item
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
st_terror1{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.8350{col 45} 0.6004{col 59} .0608668{col 73} 0.8112
{txt}st_terror2{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.8546{col 45} 0.6858{col 59} .0580719{col 73} 0.7195
{txt}st_terror3{col 14}{c |}{res}{col 16} 254{col 24}+{col 31} 0.8723{col 45} 0.7062{col 59} .0524448{col 73} 0.6939
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0571279{col 73} 0.8109
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. 
. 
. 
. 
.                                         ***I2: Civil liberties (general)***
. 
. 
. *Factor analysis
. spearman st_video st_email st_spyDE st_spyother if st_terrori!=. ///
>         & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1!=. ///
>         & german==1, stats(rho obs p)

{txt}{c TLC }{hline 17}{c TRC}
{c |}  Key{col 19}{c |}
{c LT }{hline 17}{c RT}
{c |}  {it: rho}{col 19}{c |}
{c |}  {it: Number of obs}{col 19}{c |}
{c |}  {it: Sig. level}{col 19}{c |}
{c BLC }{hline 17}{c BRC}

             {c |} st_video st_email st_spyDE st_spy~r
{hline 13}{c +}{hline 36}
    st_video {c |} {res}  1.0000 
             {txt}{c |} {res}     236 
             {txt}{c |} 
             {c |}
    st_email {c |} {res}  0.5168   1.0000 
             {txt}{c |} {res}     236      236 
             {txt}{c |} {res}  0.0000 
             {txt}{c |}
    st_spyDE {c |} {res}  0.4014   0.4773   1.0000 
             {txt}{c |} {res}     236      236      236 
             {txt}{c |} {res}  0.0000   0.0000 
             {txt}{c |}
 st_spyother {c |} {res}  0.3197   0.3727   0.7694   1.0000 
             {txt}{c |} {res}     236      236      236      236 
             {txt}{c |} {res}  0.0000   0.0000   0.0000 
             {txt}{c |}

{com}. factor st_video st_email st_spyDE st_spyother [aweight = wghtpew] ///
>         if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. ///
>         & work2!=. & treatment1!=. & german==1, pcf
{txt}(sum of wgt is   2.3304e+02)
(obs=236)

Factor analysis/correlation{col 50}Number of obs    = {res}       236
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       1
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       4

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.43470      1.56425            0.6087       0.6087
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.87046      0.40487            0.2176       0.8263
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.46559      0.23634            0.1164       0.9427
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.22925            .            0.0573       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}  347.55{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:st_video}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7032}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5056}}}{space 1}
{space 4}{space 0}{ralign 12:st_email}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7444}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4459}}}{space 1}
{space 4}{space 0}{ralign 12:st_spyDE}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8696}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2437}}}{space 1}
{space 4}{space 0}{ralign 12:st_spyother}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7936}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3702}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. alphawgt st_video st_email st_spyDE st_spyother [aweight = wghtpew] ///
>         if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. ///
>         & proedu2!=. & work2!=. & treatment1!=. & german==1, casewise item

{txt}Test scale = mean(unstandardized items)
Weights: aweight = wghtpew
                                                            average
                             item-test     item-rest      inter-item
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
st_video{col 14}{c |}{res}{col 16} 236{col 24}+{col 31} 0.7432{col 45} 0.5148{col 59} .0455543{col 73} 0.7640
{txt}st_email{col 14}{c |}{res}{col 16} 236{col 24}+{col 31} 0.7695{col 45} 0.5627{col 59}  .043102{col 73} 0.7377
{txt}st_spyDE{col 14}{c |}{res}{col 16} 236{col 24}+{col 31} 0.8381{col 45} 0.6994{col 59}  .038146{col 73} 0.6688
{txt}st_spyother{col 14}{c |}{res}{col 16} 236{col 24}+{col 31} 0.7613{col 45} 0.5740{col 59} .0448856{col 73} 0.7314
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59}  .042922{col 73} 0.7795
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. 
{txt}end of do-file

{com}. do "Appendix_J.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. 
. 
. *-------------------------------------------------------------------------------
. *Appendix J: Balance test: Stability of ethnocentrism throughout the survey period
. *-------------------------------------------------------------------------------
. 
. 
. *Ethnocentrism index variable
. gen tempmean = st_ethno
{txt}(438 missing values generated)

{com}. svy: mean tempmean
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}     3,052
{txt}{col 1}Number of PSUs{col 18}= {res}    162{txt}{col 35}Population size{col 51}={res} 3,018.9311
{txt}{col 35}Design df{col 51}= {res}       161

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}tempmean {c |}{col 14}{res}{space 2} .3883185{col 26}{space 2}  .005875{col 37}{space 5} .3767165{col 51}{space 3} .3999205
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. sort edate
{txt}
{com}. by edate: egen st_ethno_mean = mean(tempmean) 
{txt}(4 missing values generated)

{com}. by edate: egen st_ethno_sd = sd(tempmean) 
{txt}(16 missing values generated)

{com}. gen st_ethno_UB = st_ethno_mean + 1.96*st_ethno_sd/sqrt(3182)
{txt}(16 missing values generated)

{com}. gen st_ethno_LB = st_ethno_mean - 1.96*st_ethno_sd/sqrt(3182)
{txt}(16 missing values generated)

{com}. 
. 
.                                                                 ***Figure J1***
.         
. twoway (scatter st_ethno_mean edate, mcolor(gs9) msize(medsmall) msymbol(circle)) ///
> (lowess st_ethno_mean edate, lcolor(black)), ytitle(Ethnocentrism (Mean)) ///
> ytitle(, size(medium)) ylabel(0(0.2)1, labsize(medsmall) angle(horizontal)) ///
> ymtick(0(0.1)1, grid) xtitle("") xlabel(#6, labsize(medsmall) angle(stdarrow) grid) ///
> legend(order(1 "Mean of ethnocentrism per day" 2 "Lowess smooth") size(medium) ///
> margin(small) region(fcolor(none) lcolor(none)))
{res}{txt}
{com}. drop tempmean
{txt}
{com}. 
. svy: mean st_ethno if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1==0 & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       202
{txt}{col 1}Number of PSUs{col 18}= {res}     95{txt}{col 35}Population size{col 51}={res} 202.301065
{txt}{col 35}Design df{col 51}= {res}        94

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}st_ethno {c |}{col 14}{res}{space 2} .3864926{col 26}{space 2} .0145079{col 37}{space 5} .3576868{col 51}{space 3} .4152985
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 4}st_ethno {c |}{col 14}{result}{space 2} .3864926{col 27}{space 2} .1909934
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. 
. svy: mean st_ethno if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & treatment1==1 & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}        52
{txt}{col 1}Number of PSUs{col 18}= {res}     40{txt}{col 35}Population size{col 51}={res} 48.7485031
{txt}{col 35}Design df{col 51}= {res}        39

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}st_ethno {c |}{col 14}{res}{space 2} .3945561{col 26}{space 2} .0319221{col 37}{space 5} .3299876{col 51}{space 3} .4591247
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 4}st_ethno {c |}{col 14}{result}{space 2} .3945561{col 27}{space 2} .2084335
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. 
. ttest st_ethno if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)        

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
 Control {c |}{res}{col 12}    202{col 22} .3941419{col 34} .0138349{col 46} .1966308{col 58} .3668618{col 70} .4214221
{txt}Treatmen {c |}{res}{col 12}     52{col 22} .4110577{col 34} .0293878{col 46} .2119183{col 58} .3520593{col 70} .4700561
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    254{col 22}  .397605{col 34} .0125203{col 46} .1995411{col 58} .3729477{col 70} .4222623
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0169158{col 34} .0310725{col 58}-.0781107{col 70} .0442792
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Control{txt}) - mean({res}Treatmen{txt})                         t = {res} -0.5444
{txt}Ho: diff = 0                                     degrees of freedom = {res}     252

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.2933         {txt}Pr(|T| > |t|) = {res}0.5867          {txt}Pr(T > t) = {res}0.7067
{txt}
{com}. ********************************************************************************
. 
. 
. 
.                                 ***Not displayed and described in online appendix***
. 
.                                 
. *Alternative ethnocentrism measure
. tab st_homocult

{txt}st_homocult {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        970       28.04       28.04
{txt}   .3333333 {c |}{res}      1,458       42.15       70.19
{txt}   .6666667 {c |}{res}        724       20.93       91.12
{txt}          1 {c |}{res}        307        8.88      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      3,459      100.00
{txt}
{com}. sum st_homocult 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}st_homocult {c |}{res}      3,459    .3687964    .3043527          0          1
{txt}
{com}. gen tempmean = st_homocult
{txt}(31 missing values generated)

{com}. svy: mean tempmean
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}     3,459
{txt}{col 1}Number of PSUs{col 18}= {res}    162{txt}{col 35}Population size{col 51}={res} 3,459.4566
{txt}{col 35}Design df{col 51}= {res}       161

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}tempmean {c |}{col 14}{res}{space 2} .3583195{col 26}{space 2} .0081294{col 37}{space 5} .3422655{col 51}{space 3} .3743735
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. sort edate
{txt}
{com}. by edate: egen st_homocult_mean = mean(tempmean) 
{txt}
{com}. by edate: egen st_homocult_sd = sd(tempmean) 
{txt}(6 missing values generated)

{com}. gen st_homocult_UB = st_homocult_mean + 1.96*st_homocult_sd/sqrt(3182)
{txt}(6 missing values generated)

{com}. gen st_homocult_LB = st_homocult_mean - 1.96*st_homocult_sd/sqrt(3182)
{txt}(6 missing values generated)

{com}. 
.         
. twoway (scatter st_homocult_mean edate, mcolor(gs8) msize(medsmall) msymbol(circle)) ///
> (lowess st_homocult_mean edate, lcolor(black)), ytitle(Belong to common culture (Mean)) ///
> ytitle(, size(medium)) ylabel(0(0.2)1, labsize(medsmall) angle(horizontal)) ///
> ymtick(0(0.1)1, grid) xtitle("") xlabel(#6, labsize(medsmall) angle(stdarrow) grid) ///
> legend(order(1 "Mean of 'Belong to common culture'" 2 "Lowess smooth") size(medium) ///
> margin(small) region(fcolor(none) lcolor(none)))
{res}{txt}
{com}. drop tempmean
{txt}
{com}. 
. 
. *-------------------------------------------------------------------------------
. 
{txt}end of do-file

{com}. do "Appendix_K.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. 
. 
. *-------------------------------------------------------------------------------
. *Appendix K: Addressing selection: Comparability of treatment and control groups
. *-------------------------------------------------------------------------------
.                                                                 
.                                                                 
.                                                                 ****Table K1***
. 
. *Sociodemographic difference between the pre- and post-attacks groups
. *Results have den manually transferred to word table.
. 
. tab sex treatment1 if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, column
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

 RECODE of {c |}
      gndr {c |}
(GESCHLECH {c |}
        T, {c |}
BEFRAGTE<R {c |}      treatment1
        >) {c |}   Control  Treatment {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
      Male {c |}{res}       120         27 {txt}{c |}{res}       147 
           {txt}{c |}{res}     55.81      48.21 {txt}{c |}{res}     54.24 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Female {c |}{res}        95         29 {txt}{c |}{res}       124 
           {txt}{c |}{res}     44.19      51.79 {txt}{c |}{res}     45.76 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       215         56 {txt}{c |}{res}       271 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. svy: mean age if treatment1==0 & st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}       215
{txt}{col 1}Number of PSUs{col 18}= {res}     98{txt}{col 35}Population size{col 51}={res} 214.838429
{txt}{col 35}Design df{col 51}= {res}        97

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2}  53.1814{col 26}{space 2} 1.230993{col 37}{space 5} 50.73822{col 51}{space 3} 55.62458
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 9}age {c |}{col 14}{result}{space 2}  53.1814{col 27}{space 2} 18.47367
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. svy: mean age if treatment1==1 & st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1
{txt}(running mean on estimation sample)
{res}
{txt}Survey: Mean estimation

{col 1}Number of strata{col 18}= {res}      1{txt}{col 35}Number of obs{col 51}= {res}        56
{txt}{col 1}Number of PSUs{col 18}= {res}     44{txt}{col 35}Population size{col 51}={res} 52.2828567
{txt}{col 35}Design df{col 51}= {res}        43

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2} 52.53188{col 26}{space 2} 2.838463{col 37}{space 5} 46.80758{col 51}{space 3} 58.25619
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. estat sd

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{col 14}{text}{c |}       Mean{col 27}  Std. Dev.
{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}
{space 9}age {c |}{col 14}{result}{space 2} 52.53188{col 27}{space 2} 18.84886
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 0}{hline 0}{hline 0}{hline 0}

{com}. tab proedu2 treatment1 if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, column
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

     Highest level of {c |}      treatment1
            education {c |}   Control  Treatment {c |}     Total
{hline 22}{c +}{hline 22}{c +}{hline 10}
Lower secondary or le {c |}{res}        16          4 {txt}{c |}{res}        20 
                      {txt}{c |}{res}      7.44       7.14 {txt}{c |}{res}      7.38 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
      Upper secondary {c |}{res}       104         23 {txt}{c |}{res}       127 
                      {txt}{c |}{res}     48.37      41.07 {txt}{c |}{res}     46.86 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
Short tertiary educat {c |}{res}        47          9 {txt}{c |}{res}        56 
                      {txt}{c |}{res}     21.86      16.07 {txt}{c |}{res}     20.66 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
Medium to long tertia {c |}{res}        48         20 {txt}{c |}{res}        68 
                      {txt}{c |}{res}     22.33      35.71 {txt}{c |}{res}     25.09 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
                Total {c |}{res}       215         56 {txt}{c |}{res}       271 
                      {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab work2 treatment1 if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, column
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

                     {c |}      treatment1
      Working status {c |}   Control  Treatment {c |}     Total
{hline 21}{c +}{hline 22}{c +}{hline 10}
          Unemployed {c |}{res}        15          3 {txt}{c |}{res}        18 
                     {txt}{c |}{res}      6.98       5.36 {txt}{c |}{res}      6.64 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
             Working {c |}{res}       105         33 {txt}{c |}{res}       138 
                     {txt}{c |}{res}     48.84      58.93 {txt}{c |}{res}     50.92 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
             Retired {c |}{res}        73         18 {txt}{c |}{res}        91 
                     {txt}{c |}{res}     33.95      32.14 {txt}{c |}{res}     33.58 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
           Housework {c |}{res}         9          0 {txt}{c |}{res}         9 
                     {txt}{c |}{res}      4.19       0.00 {txt}{c |}{res}      3.32 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
In school or student {c |}{res}        13          2 {txt}{c |}{res}        15 
                     {txt}{c |}{res}      6.05       3.57 {txt}{c |}{res}      5.54 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
               Total {c |}{res}       215         56 {txt}{c |}{res}       271 
                     {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. 
. prtest sex if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)            

{txt}Two-sample test of proportions               {res}Control{txt}: Number of obs = {res}     215
                                           Treatment{txt}: Number of obs = {res}      56
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 6}Control{col 14}{c |}{res}{col 17} .4418605{col 28} .0338684{col 58} .3754796{col 70} .5082413
   {txt}Treatment {c |}{res}{col 17} .5178571{col 28} .0667727{col 58} .3869851{col 70} .6487292
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17}-.0759967{col 28}  .074871{col 58}-.2227411{col 70} .0707477
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0747432{col 38}   -1.02{col 49}0.309
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}Control{txt}) - prop({res}Treatment{txt})                    z = {res} -1.0168
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.1546         {txt}Pr(|Z| < |z|) = {res}0.3093          {txt}Pr(Z > z) = {res}0.8454
{txt}
{com}. ttest age if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)     

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
 Control {c |}{res}{col 12}    215{col 22} 52.85116{col 34} 1.241981{col 46} 18.21102{col 58} 50.40308{col 70} 55.29925
{txt}Treatmen {c |}{res}{col 12}     56{col 22} 54.26786{col 34} 2.443052{col 46} 18.28212{col 58} 49.37187{col 70} 59.16384
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    271{col 22} 53.14391{col 34} 1.105624{col 46} 18.20087{col 58} 50.96717{col 70} 55.32065
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-1.416694{col 34} 2.734342{col 58}-6.800126{col 70} 3.966738
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Control{txt}) - mean({res}Treatmen{txt})                         t = {res} -0.5181
{txt}Ho: diff = 0                                     degrees of freedom = {res}     269

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.3024         {txt}Pr(|T| > |t|) = {res}0.6048          {txt}Pr(T > t) = {res}0.6976
{txt}
{com}. prtest low_second if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)

{txt}Two-sample test of proportions               {res}Control{txt}: Number of obs = {res}     215
                                           Treatment{txt}: Number of obs = {res}      56
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 6}Control{col 14}{c |}{res}{col 17} .0744186{col 28}  .017899{col 58} .0393372{col 70}    .1095
   {txt}Treatment {c |}{res}{col 17} .0714286{col 28} .0344151{col 58} .0039761{col 70}  .138881
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17}   .00299{col 28} .0387915{col 58}-.0730398{col 70} .0790199
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0392242{col 38}    0.08{col 49}0.939
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}Control{txt}) - prop({res}Treatment{txt})                    z = {res}  0.0762
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.5304         {txt}Pr(|Z| < |z|) = {res}0.9392          {txt}Pr(Z > z) = {res}0.4696
{txt}
{com}. prtest upp_second if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)

{txt}Two-sample test of proportions               {res}Control{txt}: Number of obs = {res}     215
                                           Treatment{txt}: Number of obs = {res}      56
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 6}Control{col 14}{c |}{res}{col 17} .4837209{col 28} .0340816{col 58} .4169221{col 70} .5505197
   {txt}Treatment {c |}{res}{col 17} .4107143{col 28} .0657414{col 58} .2818635{col 70}  .539565
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0730066{col 28} .0740506{col 58}-.0721298{col 70} .2181431
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0748661{col 38}    0.98{col 49}0.329
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}Control{txt}) - prop({res}Treatment{txt})                    z = {res}  0.9752
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.8353         {txt}Pr(|Z| < |z|) = {res}0.3295          {txt}Pr(Z > z) = {res}0.1647
{txt}
{com}. prtest short_tert if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)

{txt}Two-sample test of proportions               {res}Control{txt}: Number of obs = {res}     215
                                           Treatment{txt}: Number of obs = {res}      56
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 6}Control{col 14}{c |}{res}{col 17} .2186047{col 28} .0281868{col 58} .1633595{col 70} .2738498
   {txt}Treatment {c |}{res}{col 17} .1607143{col 28} .0490781{col 58} .0645229{col 70} .2569057
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0578904{col 28} .0565965{col 58}-.0530367{col 70} .1688174
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0607457{col 38}    0.95{col 49}0.341
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}Control{txt}) - prop({res}Treatment{txt})                    z = {res}  0.9530
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.8297         {txt}Pr(|Z| < |z|) = {res}0.3406          {txt}Pr(Z > z) = {res}0.1703
{txt}
{com}. prtest medlong_tert if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)

{txt}Two-sample test of proportions               {res}Control{txt}: Number of obs = {res}     215
                                           Treatment{txt}: Number of obs = {res}      56
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 6}Control{col 14}{c |}{res}{col 17} .2232558{col 28} .0284002{col 58} .1675925{col 70} .2789192
   {txt}Treatment {c |}{res}{col 17} .3571429{col 28} .0640301{col 58} .2316462{col 70} .4826396
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} -.133887{col 28} .0700459{col 58}-.2711744{col 70} .0034004
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0650436{col 38}   -2.06{col 49}0.040
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}Control{txt}) - prop({res}Treatment{txt})                    z = {res} -2.0584
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.0198         {txt}Pr(|Z| < |z|) = {res}0.0395          {txt}Pr(Z > z) = {res}0.9802
{txt}
{com}. 
. prtest unemployed if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)

{txt}Two-sample test of proportions               {res}Control{txt}: Number of obs = {res}     215
                                           Treatment{txt}: Number of obs = {res}      56
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 6}Control{col 14}{c |}{res}{col 17} .0697674{col 28} .0173741{col 58} .0357148{col 70} .1038201
   {txt}Treatment {c |}{res}{col 17} .0535714{col 28} .0300896{col 58}-.0054031{col 70}  .112546
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17}  .016196{col 28} .0347454{col 58}-.0519038{col 70} .0842958
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0373593{col 38}    0.43{col 49}0.665
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}Control{txt}) - prop({res}Treatment{txt})                    z = {res}  0.4335
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.6677         {txt}Pr(|Z| < |z|) = {res}0.6646          {txt}Pr(Z > z) = {res}0.3323
{txt}
{com}. prtest working if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)

{txt}Two-sample test of proportions               {res}Control{txt}: Number of obs = {res}     215
                                           Treatment{txt}: Number of obs = {res}      56
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 6}Control{col 14}{c |}{res}{col 17} .4883721{col 28} .0340905{col 58}  .421556{col 70} .5551882
   {txt}Treatment {c |}{res}{col 17} .5892857{col 28} .0657414{col 58}  .460435{col 70} .7181365
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17}-.1009136{col 28} .0740547{col 58}-.2460581{col 70} .0442308
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0750011{col 38}   -1.35{col 49}0.178
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}Control{txt}) - prop({res}Treatment{txt})                    z = {res} -1.3455
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.0892         {txt}Pr(|Z| < |z|) = {res}0.1785          {txt}Pr(Z > z) = {res}0.9108
{txt}
{com}. prtest retired if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)

{txt}Two-sample test of proportions               {res}Control{txt}: Number of obs = {res}     215
                                           Treatment{txt}: Number of obs = {res}      56
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 6}Control{col 14}{c |}{res}{col 17} .3395349{col 28} .0322959{col 58}  .276236{col 70} .4028338
   {txt}Treatment {c |}{res}{col 17} .3214286{col 28} .0624088{col 58} .1991095{col 70} .4437476
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0181063{col 28} .0702701{col 58}-.1196206{col 70} .1558332
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0708531{col 38}    0.26{col 49}0.798
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}Control{txt}) - prop({res}Treatment{txt})                    z = {res}  0.2555
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.6008         {txt}Pr(|Z| < |z|) = {res}0.7983          {txt}Pr(Z > z) = {res}0.3992
{txt}
{com}. prtest housework if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)

{txt}Two-sample test of proportions               {res}Control{txt}: Number of obs = {res}     215
                                           Treatment{txt}: Number of obs = {res}      56
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 6}Control{col 14}{c |}{res}{col 17} .0418605{col 28} .0136583{col 58} .0150907{col 70} .0686303
   {txt}Treatment {c |}{res}{col 17}        0{col 28}        0{col 58}        0{col 70}        0
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0418605{col 28} .0136583{col 58} .0150907{col 70} .0686303
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0268828{col 38}    1.56{col 49}0.119
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}Control{txt}) - prop({res}Treatment{txt})                    z = {res}  1.5571
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9403         {txt}Pr(|Z| < |z|) = {res}0.1194          {txt}Pr(Z > z) = {res}0.0597
{txt}
{com}. prtest student if st_terrori!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. & german==1, by(treatment1)

{txt}Two-sample test of proportions               {res}Control{txt}: Number of obs = {res}     215
                                           Treatment{txt}: Number of obs = {res}      56
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 6}Control{col 14}{c |}{res}{col 17} .0604651{col 28} .0162551{col 58} .0286057{col 70} .0923245
   {txt}Treatment {c |}{res}{col 17} .0357143{col 28} .0247988{col 58}-.0128904{col 70} .0843189
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0247508{col 28} .0296514{col 58}-.0333649{col 70} .0828665
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0343058{col 38}    0.72{col 49}0.471
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}Control{txt}) - prop({res}Treatment{txt})                    z = {res}  0.7215
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.7647         {txt}Pr(|Z| < |z|) = {res}0.4706          {txt}Pr(Z > z) = {res}0.2353
{txt}
{com}. 
. 
. ********************************************************************************
. 
.                                 ***Described but not displayed in online appendix***
. 
. 
. *Men
. svy: regress st_terrori c.st_ethno##i.treatment1 st_age st_age2 i.proedu2 i.work2 if sex==0
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       141
{txt}{col 1}Number of PSUs{col 20}= {res}       89{txt}{col 49}Population size{col 67}={res} 136.143829
{txt}{col 49}Design df{col 67}= {res}        88
{txt}{col 49}F({res}  11{txt},{res}     78{txt}){col 67}= {res}      2.99
{txt}{col 49}Prob > F{col 67}= {res}    0.0023
{txt}{col 49}R-squared{col 67}= {res}    0.1717

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2}-.1482479{col 48}{space 2} .1350302{col 59}{space 1}   -1.10{col 68}{space 3}0.275{col 76}{space 4}-.4165921{col 89}{space 3} .1200963
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1354996{col 48}{space 2} .0880501{col 59}{space 1}   -1.54{col 68}{space 3}0.127{col 76}{space 4}-.3104806{col 89}{space 3} .0394814
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .5853796{col 48}{space 2} .1784342{col 59}{space 1}    3.28{col 68}{space 3}0.001{col 76}{space 4} .2307792{col 89}{space 3}   .93998
{txt}{space 34} {c |}
{space 28}st_age {c |}{col 36}{res}{space 2} .9003575{col 48}{space 2} .3851439{col 59}{space 1}    2.34{col 68}{space 3}0.022{col 76}{space 4}  .134965{col 89}{space 3}  1.66575
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6615538{col 48}{space 2} .3954528{col 59}{space 1}   -1.67{col 68}{space 3}0.098{col 76}{space 4}-1.447433{col 89}{space 3} .1243256
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} .0186243{col 48}{space 2} .1078109{col 59}{space 1}    0.17{col 68}{space 3}0.863{col 76}{space 4}-.1956273{col 89}{space 3} .2328759
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .1227545{col 48}{space 2} .1254611{col 59}{space 1}    0.98{col 68}{space 3}0.331{col 76}{space 4} -.126573{col 89}{space 3}  .372082
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} .0419877{col 48}{space 2} .1216281{col 59}{space 1}    0.35{col 68}{space 3}0.731{col 76}{space 4}-.1997225{col 89}{space 3} .2836979
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.2651574{col 48}{space 2} .1284738{col 59}{space 1}   -2.06{col 68}{space 3}0.042{col 76}{space 4} -.520472{col 89}{space 3}-.0098427
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.3100439{col 48}{space 2} .1356891{col 59}{space 1}   -2.28{col 68}{space 3}0.025{col 76}{space 4}-.5796976{col 89}{space 3}-.0403903
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1725415{col 48}{space 2} .1760981{col 59}{space 1}   -0.98{col 68}{space 3}0.330{col 76}{space 4}-.5224994{col 89}{space 3} .1774164
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5685488{col 48}{space 2} .1673058{col 59}{space 1}    3.40{col 68}{space 3}0.001{col 76}{space 4} .2360636{col 89}{space 3}  .901034
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label(proper) replace
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. *Women
. svy: regress st_terrori c.st_ethno##i.treatment1 st_age st_age2 i.proedu2 i.work2 if sex==1
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       113
{txt}{col 1}Number of PSUs{col 20}= {res}       76{txt}{col 49}Population size{col 67}={res} 114.905738
{txt}{col 49}Design df{col 67}= {res}        75
{txt}{col 49}F({res}  12{txt},{res}     64{txt}){col 67}= {res}      3.40
{txt}{col 49}Prob > F{col 67}= {res}    0.0007
{txt}{col 49}R-squared{col 67}= {res}    0.2131

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2305746{col 48}{space 2} .1562191{col 59}{space 1}    1.48{col 68}{space 3}0.144{col 76}{space 4}-.0806298{col 89}{space 3}  .541779
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.0249237{col 48}{space 2} .1076097{col 59}{space 1}   -0.23{col 68}{space 3}0.817{col 76}{space 4}-.2392932{col 89}{space 3} .1894458
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .4816797{col 48}{space 2}   .24493{col 59}{space 1}    1.97{col 68}{space 3}0.053{col 76}{space 4}-.0062459{col 89}{space 3} .9696053
{txt}{space 34} {c |}
{space 28}st_age {c |}{col 36}{res}{space 2} .3670408{col 48}{space 2} .4699785{col 59}{space 1}    0.78{col 68}{space 3}0.437{col 76}{space 4}-.5692044{col 89}{space 3} 1.303286
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6574182{col 48}{space 2} .6245147{col 59}{space 1}   -1.05{col 68}{space 3}0.296{col 76}{space 4}-1.901515{col 89}{space 3} .5866789
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1108758{col 48}{space 2} .0706338{col 59}{space 1}   -1.57{col 68}{space 3}0.121{col 76}{space 4}-.2515855{col 89}{space 3}  .029834
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0838452{col 48}{space 2} .0795764{col 59}{space 1}   -1.05{col 68}{space 3}0.295{col 76}{space 4}-.2423696{col 89}{space 3} .0746792
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.2059741{col 48}{space 2} .0928999{col 59}{space 1}   -2.22{col 68}{space 3}0.030{col 76}{space 4}-.3910403{col 89}{space 3} -.020908
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0576139{col 48}{space 2} .0610585{col 59}{space 1}    0.94{col 68}{space 3}0.348{col 76}{space 4}-.0640208{col 89}{space 3} .1792486
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .1316537{col 48}{space 2} .1012133{col 59}{space 1}    1.30{col 68}{space 3}0.197{col 76}{space 4}-.0699735{col 89}{space 3}  .333281
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1339857{col 48}{space 2} .1137694{col 59}{space 1}    1.18{col 68}{space 3}0.243{col 76}{space 4}-.0926545{col 89}{space 3}  .360626
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1068242{col 48}{space 2} .1325307{col 59}{space 1}   -0.81{col 68}{space 3}0.423{col 76}{space 4}-.3708389{col 89}{space 3} .1571906
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .4738475{col 48}{space 2} .1334682{col 59}{space 1}    3.55{col 68}{space 3}0.001{col 76}{space 4} .2079652{col 89}{space 3} .7397299
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label(proper) append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. 
. *Education
. *Less than medium to long tertiary
. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if proedu2<=3
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       188
{txt}{col 1}Number of PSUs{col 20}= {res}       99{txt}{col 49}Population size{col 67}={res} 186.428443
{txt}{col 49}Design df{col 67}= {res}        98
{txt}{col 49}F({res}  12{txt},{res}     87{txt}){col 67}= {res}      3.15
{txt}{col 49}Prob > F{col 67}= {res}    0.0009
{txt}{col 49}R-squared{col 67}= {res}    0.1382

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}  Linearized
{col 1}               st_terrori{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      t{col 59}   P>|t|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}st_ethno {c |}{col 27}{res}{space 2}-.0295192{col 39}{space 2} .1125349{col 50}{space 1}   -0.26{col 59}{space 3}0.794{col 67}{space 4} -.252841{col 80}{space 3} .1938026
{txt}{space 25} {c |}
{space 15}treatment1 {c |}
{space 15}Treatment  {c |}{col 27}{res}{space 2}-.0911753{col 39}{space 2} .1030612{col 50}{space 1}   -0.88{col 59}{space 3}0.378{col 67}{space 4}-.2956969{col 80}{space 3} .1133462
{txt}{space 25} {c |}
{space 4}treatment1#c.st_ethno {c |}
{space 15}Treatment  {c |}{col 27}{res}{space 2}  .509341{col 39}{space 2} .2002653{col 50}{space 1}    2.54{col 59}{space 3}0.013{col 67}{space 4}  .111921{col 80}{space 3} .9067609
{txt}{space 25} {c |}
{space 22}sex {c |}
{space 18}Female  {c |}{col 27}{res}{space 2} .0188976{col 39}{space 2} .0411425{col 50}{space 1}    0.46{col 59}{space 3}0.647{col 67}{space 4}-.0627484{col 80}{space 3} .1005436
{txt}{space 19}st_age {c |}{col 27}{res}{space 2} 1.051924{col 39}{space 2} .3686273{col 50}{space 1}    2.85{col 59}{space 3}0.005{col 67}{space 4}  .320395{col 80}{space 3} 1.783453
{txt}{space 18}st_age2 {c |}{col 27}{res}{space 2}-1.074695{col 39}{space 2} .4322116{col 50}{space 1}   -2.49{col 59}{space 3}0.015{col 67}{space 4}-1.932405{col 80}{space 3}-.2169855
{txt}{space 25} {c |}
{space 18}proedu2 {c |}
{space 9}Upper secondary  {c |}{col 27}{res}{space 2} -.092672{col 39}{space 2} .0663656{col 50}{space 1}   -1.40{col 59}{space 3}0.166{col 67}{space 4}-.2243723{col 80}{space 3} .0390284
{txt}Short tertiary education  {c |}{col 27}{res}{space 2}-.0177169{col 39}{space 2}  .080501{col 50}{space 1}   -0.22{col 59}{space 3}0.826{col 67}{space 4}-.1774685{col 80}{space 3} .1420347
{txt}{space 25} {c |}
{space 20}work2 {c |}
{space 17}Working  {c |}{col 27}{res}{space 2}-.1784475{col 39}{space 2} .0675936{col 50}{space 1}   -2.64{col 59}{space 3}0.010{col 67}{space 4}-.3125847{col 80}{space 3}-.0443103
{txt}{space 17}Retired  {c |}{col 27}{res}{space 2}-.1707725{col 39}{space 2} .1055611{col 50}{space 1}   -1.62{col 59}{space 3}0.109{col 67}{space 4}-.3802551{col 80}{space 3} .0387101
{txt}{space 15}Housework  {c |}{col 27}{res}{space 2}-.1170971{col 39}{space 2} .1285938{col 50}{space 1}   -0.91{col 59}{space 3}0.365{col 67}{space 4}-.3722873{col 80}{space 3} .1380931
{txt}{space 4}In school or student  {c |}{col 27}{res}{space 2}-.1257335{col 39}{space 2} .1098886{col 50}{space 1}   -1.14{col 59}{space 3}0.255{col 67}{space 4}-.3438038{col 80}{space 3} .0923367
{txt}{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2} .5709153{col 39}{space 2} .1054818{col 50}{space 1}    5.41{col 59}{space 3}0.000{col 67}{space 4} .3615901{col 80}{space 3} .7802404
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label(proper) replace
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. *Medium to long tertiary
. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if proedu2==4
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}        66
{txt}{col 1}Number of PSUs{col 20}= {res}       50{txt}{col 49}Population size{col 67}={res} 64.6211245
{txt}{col 49}Design df{col 67}= {res}        49
{txt}{col 49}F({res}  10{txt},{res}     40{txt}){col 67}= {res}    121.77
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.3264

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2}-.0999713{col 48}{space 2} .1659806{col 59}{space 1}   -0.60{col 68}{space 3}0.550{col 76}{space 4}-.4335217{col 89}{space 3} .2335792
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.2129636{col 48}{space 2} .0962684{col 59}{space 1}   -2.21{col 68}{space 3}0.032{col 76}{space 4}-.4064222{col 89}{space 3} -.019505
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .8898525{col 48}{space 2}  .278485{col 59}{space 1}    3.20{col 68}{space 3}0.002{col 76}{space 4} .3302159{col 89}{space 3} 1.449489
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.1075108{col 48}{space 2} .0681991{col 59}{space 1}   -1.58{col 68}{space 3}0.121{col 76}{space 4}-.2445621{col 89}{space 3} .0295405
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}-.1957096{col 48}{space 2} .5400875{col 59}{space 1}   -0.36{col 68}{space 3}0.719{col 76}{space 4}-1.281056{col 89}{space 3} .8896369
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} .3040803{col 48}{space 2} .5508135{col 59}{space 1}    0.55{col 68}{space 3}0.583{col 76}{space 4}-.8028208{col 89}{space 3} 1.410981
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
Medium to long tertiary education  {c |}{col 36}{res}{space 2}        0{col 48}{txt}  (omitted)
{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .2286146{col 48}{space 2}  .053575{col 59}{space 1}    4.27{col 68}{space 3}0.000{col 76}{space 4} .1209516{col 89}{space 3} .3362775
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .1692431{col 48}{space 2}  .081216{col 59}{space 1}    2.08{col 68}{space 3}0.042{col 76}{space 4} .0060334{col 89}{space 3} .3324527
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .4416223{col 48}{space 2} .0744379{col 59}{space 1}    5.93{col 68}{space 3}0.000{col 76}{space 4} .2920338{col 89}{space 3} .5912108
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1128015{col 48}{space 2} .2092018{col 59}{space 1}   -0.54{col 68}{space 3}0.592{col 76}{space 4}-.5332082{col 89}{space 3} .3076053
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .3836935{col 48}{space 2} .1364589{col 59}{space 1}    2.81{col 68}{space 3}0.007{col 76}{space 4} .1094691{col 89}{space 3}  .657918
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label(proper) append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. 
. *Working
. *Non-working
. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if work2!=2
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       124
{txt}{col 1}Number of PSUs{col 20}= {res}       79{txt}{col 49}Population size{col 67}={res} 125.675926
{txt}{col 49}Design df{col 67}= {res}        78
{txt}{col 49}F({res}  12{txt},{res}     67{txt}){col 67}= {res}      2.69
{txt}{col 49}Prob > F{col 67}= {res}    0.0051
{txt}{col 49}R-squared{col 67}= {res}    0.1523

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2}-.1464413{col 48}{space 2} .1291269{col 59}{space 1}   -1.13{col 68}{space 3}0.260{col 76}{space 4}-.4035133{col 89}{space 3} .1106306
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1274429{col 48}{space 2} .1426932{col 59}{space 1}   -0.89{col 68}{space 3}0.375{col 76}{space 4}-.4115233{col 89}{space 3} .1566376
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6062961{col 48}{space 2} .2541392{col 59}{space 1}    2.39{col 68}{space 3}0.019{col 76}{space 4} .1003439{col 89}{space 3} 1.112248
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0583146{col 48}{space 2} .0486592{col 59}{space 1}   -1.20{col 68}{space 3}0.234{col 76}{space 4}-.1551876{col 89}{space 3} .0385583
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}-.0679633{col 48}{space 2} .4104649{col 59}{space 1}   -0.17{col 68}{space 3}0.869{col 76}{space 4}-.8851362{col 89}{space 3} .7492096
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.0439912{col 48}{space 2} .4176718{col 59}{space 1}   -0.11{col 68}{space 3}0.916{col 76}{space 4}-.8755119{col 89}{space 3} .7875295
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1302412{col 48}{space 2} .0714543{col 59}{space 1}   -1.82{col 68}{space 3}0.072{col 76}{space 4}-.2724958{col 89}{space 3} .0120134
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0230068{col 48}{space 2} .0852434{col 59}{space 1}   -0.27{col 68}{space 3}0.788{col 76}{space 4}-.1927135{col 89}{space 3} .1466998
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1710252{col 48}{space 2} .0775528{col 59}{space 1}   -2.21{col 68}{space 3}0.030{col 76}{space 4} -.325421{col 89}{space 3}-.0166294
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Retired  {c |}{col 36}{res}{space 2}-.0151738{col 48}{space 2} .0867219{col 59}{space 1}   -0.17{col 68}{space 3}0.862{col 76}{space 4}-.1878239{col 89}{space 3} .1574762
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}  .068229{col 48}{space 2}  .111713{col 59}{space 1}    0.61{col 68}{space 3}0.543{col 76}{space 4}-.1541745{col 89}{space 3} .2906326
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.2202923{col 48}{space 2}  .115382{col 59}{space 1}   -1.91{col 68}{space 3}0.060{col 76}{space 4}-.4500002{col 89}{space 3} .0094155
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .8162822{col 48}{space 2} .1215686{col 59}{space 1}    6.71{col 68}{space 3}0.000{col 76}{space 4} .5742577{col 89}{space 3} 1.058307
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label(proper) replace
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. 
. *Working
. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if work2==2
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       130
{txt}{col 1}Number of PSUs{col 20}= {res}       83{txt}{col 49}Population size{col 67}={res} 125.373642
{txt}{col 49}Design df{col 67}= {res}        82
{txt}{col 49}F({res}   9{txt},{res}     74{txt}){col 67}= {res}      3.84
{txt}{col 49}Prob > F{col 67}= {res}    0.0005
{txt}{col 49}R-squared{col 67}= {res}    0.1790

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2}  .143604{col 48}{space 2} .1536285{col 59}{space 1}    0.93{col 68}{space 3}0.353{col 76}{space 4} -.162012{col 89}{space 3} .4492199
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1470907{col 48}{space 2} .0918929{col 59}{space 1}   -1.60{col 68}{space 3}0.113{col 76}{space 4} -.329895{col 89}{space 3} .0357135
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}  .691526{col 48}{space 2} .2372015{col 59}{space 1}    2.92{col 68}{space 3}0.005{col 76}{space 4} .2196567{col 89}{space 3} 1.163395
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0291812{col 48}{space 2} .0534414{col 59}{space 1}    0.55{col 68}{space 3}0.587{col 76}{space 4}-.0771307{col 89}{space 3} .1354931
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7561158{col 48}{space 2} .6618053{col 59}{space 1}    1.14{col 68}{space 3}0.257{col 76}{space 4}-.5604257{col 89}{space 3} 2.072657
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6014627{col 48}{space 2} .9628987{col 59}{space 1}   -0.62{col 68}{space 3}0.534{col 76}{space 4}-2.516975{col 89}{space 3} 1.314049
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0314311{col 48}{space 2} .1316508{col 59}{space 1}   -0.24{col 68}{space 3}0.812{col 76}{space 4}-.2933265{col 89}{space 3} .2304644
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0428949{col 48}{space 2} .1491315{col 59}{space 1}    0.29{col 68}{space 3}0.774{col 76}{space 4}-.2537751{col 89}{space 3} .3395649
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0128957{col 48}{space 2} .1437681{col 59}{space 1}   -0.09{col 68}{space 3}0.929{col 76}{space 4}-.2988963{col 89}{space 3}  .273105
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}        0{col 48}{txt}  (omitted)
{space 29}_cons {c |}{col 36}{res}{space 2} .2985444{col 48}{space 2} .1467324{col 59}{space 1}    2.03{col 68}{space 3}0.045{col 76}{space 4} .0066469{col 89}{space 3} .5904419
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label(proper) append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}. 
. *-------------------------------------------------------------------------------
. 
{txt}end of do-file

{com}. do "Appendix_L.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. 
. *-------------------------------------------------------------------------------
. *Appendix L: Supplementary analysis: detailed aspects of results
. *-------------------------------------------------------------------------------
. 
. 
. ********************************************************************************
. *L1 Findings for sociodemographic variables
. ********************************************************************************
. 
. 
.                                                                         *Table L1*
.                                         ***Results with sociodemographic variables***
. 
. *Full ethnocentrism index and civil liberties (with reference to terrorism)
. svy: regress st_terrori c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  11{txt},{res}    106{txt}){col 67}= {res}      1.57
{txt}{col 49}Prob > F{col 67}= {res}    0.1189
{txt}{col 49}R-squared{col 67}= {res}    0.0655

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1501529{col 48}{space 2} .0929733{col 59}{space 1}    1.62{col 68}{space 3}0.109{col 76}{space 4}-.0339924{col 89}{space 3} .3342982
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0025677{col 48}{space 2} .0379794{col 59}{space 1}   -0.07{col 68}{space 3}0.946{col 76}{space 4}-.0777907{col 89}{space 3} .0726553
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5848022{col 48}{space 2} .3411749{col 59}{space 1}    1.71{col 68}{space 3}0.089{col 76}{space 4}-.0909377{col 89}{space 3} 1.260542
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5101207{col 48}{space 2} .3801437{col 59}{space 1}   -1.34{col 68}{space 3}0.182{col 76}{space 4}-1.263043{col 89}{space 3} .2428019
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1233617{col 48}{space 2} .0712133{col 59}{space 1}   -1.73{col 68}{space 3}0.086{col 76}{space 4}-.2644086{col 89}{space 3} .0176851
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0583949{col 48}{space 2} .0828542{col 59}{space 1}   -0.70{col 68}{space 3}0.482{col 76}{space 4}-.2224982{col 89}{space 3} .1057083
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1282082{col 48}{space 2}  .081198{col 59}{space 1}   -1.58{col 68}{space 3}0.117{col 76}{space 4}-.2890312{col 89}{space 3} .0326147
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0432883{col 48}{space 2} .0790515{col 59}{space 1}   -0.55{col 68}{space 3}0.585{col 76}{space 4}-.1998598{col 89}{space 3} .1132832
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0730172{col 48}{space 2} .0942152{col 59}{space 1}   -0.78{col 68}{space 3}0.440{col 76}{space 4}-.2596223{col 89}{space 3}  .113588
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0062872{col 48}{space 2} .1222735{col 59}{space 1}    0.05{col 68}{space 3}0.959{col 76}{space 4}-.2358908{col 89}{space 3} .2484653
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0742073{col 48}{space 2} .1202974{col 59}{space 1}   -0.62{col 68}{space 3}0.539{col 76}{space 4}-.3124714{col 89}{space 3} .1640568
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5182031{col 48}{space 2} .1105567{col 59}{space 1}    4.69{col 68}{space 3}0.000{col 76}{space 4} .2992317{col 89}{space 3} .7371745
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  12{txt},{res}    105{txt}){col 67}= {res}      1.91
{txt}{col 49}Prob > F{col 67}= {res}    0.0412
{txt}{col 49}R-squared{col 67}= {res}    0.0930

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1406618{col 48}{space 2} .0913115{col 59}{space 1}    1.54{col 68}{space 3}0.126{col 76}{space 4}-.0401921{col 89}{space 3} .3215157
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1139349{col 48}{space 2} .0401856{col 59}{space 1}    2.84{col 68}{space 3}0.005{col 76}{space 4} .0343422{col 89}{space 3} .1935275
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} -.010819{col 48}{space 2} .0380031{col 59}{space 1}   -0.28{col 68}{space 3}0.776{col 76}{space 4}-.0860889{col 89}{space 3} .0644509
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .607384{col 48}{space 2} .3333252{col 59}{space 1}    1.82{col 68}{space 3}0.071{col 76}{space 4}-.0528085{col 89}{space 3} 1.267577
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5515108{col 48}{space 2} .3678571{col 59}{space 1}   -1.50{col 68}{space 3}0.137{col 76}{space 4}-1.280098{col 89}{space 3} .1770764
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1169071{col 48}{space 2} .0659235{col 59}{space 1}   -1.77{col 68}{space 3}0.079{col 76}{space 4}-.2474769{col 89}{space 3} .0136628
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0507394{col 48}{space 2} .0803026{col 59}{space 1}   -0.63{col 68}{space 3}0.529{col 76}{space 4}-.2097887{col 89}{space 3} .1083099
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1371289{col 48}{space 2} .0776694{col 59}{space 1}   -1.77{col 68}{space 3}0.080{col 76}{space 4}-.2909628{col 89}{space 3} .0167051
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0578587{col 48}{space 2} .0831142{col 59}{space 1}   -0.70{col 68}{space 3}0.488{col 76}{space 4}-.2224769{col 89}{space 3} .1067595
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0717269{col 48}{space 2} .0988857{col 59}{space 1}   -0.73{col 68}{space 3}0.470{col 76}{space 4}-.2675825{col 89}{space 3} .1241287
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}  .022431{col 48}{space 2} .1260644{col 59}{space 1}    0.18{col 68}{space 3}0.859{col 76}{space 4}-.2272555{col 89}{space 3} .2721176
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0816856{col 48}{space 2} .1208096{col 59}{space 1}   -0.68{col 68}{space 3}0.500{col 76}{space 4}-.3209643{col 89}{space 3}  .157593
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5083731{col 48}{space 2}  .108875{col 59}{space 1}    4.67{col 68}{space 3}0.000{col 76}{space 4} .2927324{col 89}{space 3} .7240138
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      4.88
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1330

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0007221{col 48}{space 2} .0977606{col 59}{space 1}    0.01{col 68}{space 3}0.994{col 76}{space 4}-.1929051{col 89}{space 3} .1943494
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1518117{col 48}{space 2}  .070707{col 59}{space 1}   -2.15{col 68}{space 3}0.034{col 76}{space 4}-.2918558{col 89}{space 3}-.0117676
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6750141{col 48}{space 2} .1507665{col 59}{space 1}    4.48{col 68}{space 3}0.000{col 76}{space 4}  .376402{col 89}{space 3} .9736263
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0061306{col 48}{space 2} .0367219{col 59}{space 1}   -0.17{col 68}{space 3}0.868{col 76}{space 4}-.0788629{col 89}{space 3} .0666017
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7003869{col 48}{space 2} .3186851{col 59}{space 1}    2.20{col 68}{space 3}0.030{col 76}{space 4} .0691909{col 89}{space 3} 1.331583
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6554761{col 48}{space 2} .3602407{col 59}{space 1}   -1.82{col 68}{space 3}0.071{col 76}{space 4}-1.368978{col 89}{space 3} .0580259
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0975267{col 48}{space 2} .0645064{col 59}{space 1}   -1.51{col 68}{space 3}0.133{col 76}{space 4}-.2252897{col 89}{space 3} .0302363
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}    -.022{col 48}{space 2} .0792046{col 59}{space 1}   -0.28{col 68}{space 3}0.782{col 76}{space 4}-.1788748{col 89}{space 3} .1348747
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1095112{col 48}{space 2} .0748847{col 59}{space 1}   -1.46{col 68}{space 3}0.146{col 76}{space 4}-.2578299{col 89}{space 3} .0388074
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0841434{col 48}{space 2} .0728254{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2283832{col 89}{space 3} .0600964
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1028858{col 48}{space 2} .0890782{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2793165{col 89}{space 3} .0735448
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0070997{col 48}{space 2} .1194672{col 59}{space 1}   -0.06{col 68}{space 3}0.953{col 76}{space 4}-.2437196{col 89}{space 3} .2295203
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1023694{col 48}{space 2} .1167093{col 59}{space 1}   -0.88{col 68}{space 3}0.382{col 76}{space 4}-.3335268{col 89}{space 3}  .128788
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5497256{col 48}{space 2}    .1046{col 59}{space 1}    5.26{col 68}{space 3}0.000{col 76}{space 4} .3425521{col 89}{space 3} .7568991
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. *Full ethnocentrism index and civil liberties (general)
. svy: regress st_surveillanceindex c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       249
{txt}{col 1}Number of PSUs{col 20}= {res}      116{txt}{col 49}Population size{col 67}={res} 245.580911
{txt}{col 49}Design df{col 67}= {res}       115
{txt}{col 49}F({res}  11{txt},{res}    105{txt}){col 67}= {res}      3.97
{txt}{col 49}Prob > F{col 67}= {res}    0.0001
{txt}{col 49}R-squared{col 67}= {res}    0.1106

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}              st_surveillanceindex{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1658994{col 48}{space 2} .0926731{col 59}{space 1}    1.79{col 68}{space 3}0.076{col 76}{space 4}-.0176682{col 89}{space 3}  .349467
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0689045{col 48}{space 2} .0301505{col 59}{space 1}   -2.29{col 68}{space 3}0.024{col 76}{space 4}-.1286269{col 89}{space 3}-.0091821
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4322931{col 48}{space 2} .2867078{col 59}{space 1}    1.51{col 68}{space 3}0.134{col 76}{space 4}-.1356199{col 89}{space 3} 1.000206
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2643461{col 48}{space 2} .3182308{col 59}{space 1}   -0.83{col 68}{space 3}0.408{col 76}{space 4}   -.8947{col 89}{space 3} .3660078
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0302366{col 48}{space 2} .0600435{col 59}{space 1}   -0.50{col 68}{space 3}0.616{col 76}{space 4}-.1491713{col 89}{space 3} .0886981
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0500779{col 48}{space 2} .0605839{col 59}{space 1}    0.83{col 68}{space 3}0.410{col 76}{space 4}-.0699271{col 89}{space 3} .1700829
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0470596{col 48}{space 2} .0668738{col 59}{space 1}   -0.70{col 68}{space 3}0.483{col 76}{space 4}-.1795237{col 89}{space 3} .0854045
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0521103{col 48}{space 2} .0772411{col 59}{space 1}    0.67{col 68}{space 3}0.501{col 76}{space 4}-.1008894{col 89}{space 3} .2051101
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0370638{col 48}{space 2} .0819008{col 59}{space 1}    0.45{col 68}{space 3}0.652{col 76}{space 4}-.1251659{col 89}{space 3} .1992935
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1509977{col 48}{space 2} .1342967{col 59}{space 1}    1.12{col 68}{space 3}0.263{col 76}{space 4}-.1150183{col 89}{space 3} .4170136
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0643622{col 48}{space 2} .0849095{col 59}{space 1}    0.76{col 68}{space 3}0.450{col 76}{space 4}-.1038273{col 89}{space 3} .2325516
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .2206989{col 48}{space 2} .0891197{col 59}{space 1}    2.48{col 68}{space 3}0.015{col 76}{space 4} .0441698{col 89}{space 3} .3972279
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_surveillanceindex c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       249
{txt}{col 1}Number of PSUs{col 20}= {res}      116{txt}{col 49}Population size{col 67}={res} 245.580911
{txt}{col 49}Design df{col 67}= {res}       115
{txt}{col 49}F({res}  12{txt},{res}    104{txt}){col 67}= {res}      5.40
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1500

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}              st_surveillanceindex{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1577794{col 48}{space 2} .0932385{col 59}{space 1}    1.69{col 68}{space 3}0.093{col 76}{space 4}-.0269082{col 89}{space 3}  .342467
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1209177{col 48}{space 2} .0383475{col 59}{space 1}    3.15{col 68}{space 3}0.002{col 76}{space 4} .0449586{col 89}{space 3} .1968768
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0727966{col 48}{space 2} .0300061{col 59}{space 1}   -2.43{col 68}{space 3}0.017{col 76}{space 4} -.132233{col 89}{space 3}-.0133603
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4855974{col 48}{space 2} .2800014{col 59}{space 1}    1.73{col 68}{space 3}0.086{col 76}{space 4}-.0690314{col 89}{space 3} 1.040226
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.3386412{col 48}{space 2} .3159996{col 59}{space 1}   -1.07{col 68}{space 3}0.286{col 76}{space 4}-.9645756{col 89}{space 3} .2872933
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0299656{col 48}{space 2} .0569168{col 59}{space 1}   -0.53{col 68}{space 3}0.600{col 76}{space 4}-.1427068{col 89}{space 3} .0827756
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0522146{col 48}{space 2} .0591151{col 59}{space 1}    0.88{col 68}{space 3}0.379{col 76}{space 4} -.064881{col 89}{space 3} .1693103
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}  -.06093{col 48}{space 2} .0649052{col 59}{space 1}   -0.94{col 68}{space 3}0.350{col 76}{space 4}-.1894948{col 89}{space 3} .0676348
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0398564{col 48}{space 2} .0808809{col 59}{space 1}    0.49{col 68}{space 3}0.623{col 76}{space 4}-.1203532{col 89}{space 3}  .200066
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0380864{col 48}{space 2} .0863007{col 59}{space 1}    0.44{col 68}{space 3}0.660{col 76}{space 4}-.1328586{col 89}{space 3} .2090315
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1672217{col 48}{space 2} .1379936{col 59}{space 1}    1.21{col 68}{space 3}0.228{col 76}{space 4} -.106117{col 89}{space 3} .4405603
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0641553{col 48}{space 2} .0865277{col 59}{space 1}    0.74{col 68}{space 3}0.460{col 76}{space 4}-.1072393{col 89}{space 3} .2355499
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .2062342{col 48}{space 2} .0889083{col 59}{space 1}    2.32{col 68}{space 3}0.022{col 76}{space 4} .0301239{col 89}{space 3} .3823445
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_surveillanceindex c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       249
{txt}{col 1}Number of PSUs{col 20}= {res}      116{txt}{col 49}Population size{col 67}={res} 245.580911
{txt}{col 49}Design df{col 67}= {res}       115
{txt}{col 49}F({res}  13{txt},{res}    103{txt}){col 67}= {res}      5.10
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1502

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}              st_surveillanceindex{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1503286{col 48}{space 2} .1050585{col 59}{space 1}    1.43{col 68}{space 3}0.155{col 76}{space 4}-.0577721{col 89}{space 3} .3584294
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1069277{col 48}{space 2} .0756068{col 59}{space 1}    1.41{col 68}{space 3}0.160{col 76}{space 4}-.0428349{col 89}{space 3} .2566903
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .0365302{col 48}{space 2} .1699852{col 59}{space 1}    0.21{col 68}{space 3}0.830{col 76}{space 4}-.3001778{col 89}{space 3} .3732382
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}  -.07246{col 48}{space 2} .0301241{col 59}{space 1}   -2.41{col 68}{space 3}0.018{col 76}{space 4}-.1321301{col 89}{space 3}-.0127898
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4911836{col 48}{space 2} .2830537{col 59}{space 1}    1.74{col 68}{space 3}0.085{col 76}{space 4}-.0694912{col 89}{space 3} 1.051859
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.3446094{col 48}{space 2} .3195979{col 59}{space 1}   -1.08{col 68}{space 3}0.283{col 76}{space 4}-.9776712{col 89}{space 3} .2884525
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0292649{col 48}{space 2} .0563266{col 59}{space 1}   -0.52{col 68}{space 3}0.604{col 76}{space 4}-.1408371{col 89}{space 3} .0823072
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0532519{col 48}{space 2} .0585186{col 59}{space 1}    0.91{col 68}{space 3}0.365{col 76}{space 4}-.0626623{col 89}{space 3}  .169166
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0599279{col 48}{space 2} .0639056{col 59}{space 1}   -0.94{col 68}{space 3}0.350{col 76}{space 4}-.1865125{col 89}{space 3} .0666567
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0385478{col 48}{space 2}  .080527{col 59}{space 1}    0.48{col 68}{space 3}0.633{col 76}{space 4}-.1209606{col 89}{space 3} .1980563
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0363332{col 48}{space 2} .0862067{col 59}{space 1}    0.42{col 68}{space 3}0.674{col 76}{space 4}-.1344257{col 89}{space 3}  .207092
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1655713{col 48}{space 2} .1379152{col 59}{space 1}    1.20{col 68}{space 3}0.232{col 76}{space 4}-.1076121{col 89}{space 3} .4387547
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0628653{col 48}{space 2} .0860476{col 59}{space 1}    0.73{col 68}{space 3}0.467{col 76}{space 4}-.1075784{col 89}{space 3}  .233309
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .208489{col 48}{space 2} .0896122{col 59}{space 1}    2.33{col 68}{space 3}0.022{col 76}{space 4} .0309845{col 89}{space 3} .3859935
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. *Authorchild and civil liberties with reference to terrorism
. svy: regress st_terrori c.st_authorchild i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       271
{txt}{col 1}Number of PSUs{col 20}= {res}      122{txt}{col 49}Population size{col 67}={res} 267.121285
{txt}{col 49}Design df{col 67}= {res}       121
{txt}{col 49}F({res}  11{txt},{res}    111{txt}){col 67}= {res}      1.45
{txt}{col 49}Prob > F{col 67}= {res}    0.1598
{txt}{col 49}R-squared{col 67}= {res}    0.0505

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.0587124{col 48}{space 2} .0767007{col 59}{space 1}   -0.77{col 68}{space 3}0.445{col 76}{space 4}-.2105618{col 89}{space 3} .0931369
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0072634{col 48}{space 2} .0361849{col 59}{space 1}    0.20{col 68}{space 3}0.841{col 76}{space 4}-.0643741{col 89}{space 3} .0789009
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .3993989{col 48}{space 2}  .308688{col 59}{space 1}    1.29{col 68}{space 3}0.198{col 76}{space 4}-.2117305{col 89}{space 3} 1.010528
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2566153{col 48}{space 2}  .353539{col 59}{space 1}   -0.73{col 68}{space 3}0.469{col 76}{space 4} -.956539{col 89}{space 3} .4433084
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1520509{col 48}{space 2} .0695769{col 59}{space 1}   -2.19{col 68}{space 3}0.031{col 76}{space 4}-.2897968{col 89}{space 3}-.0143051
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1009689{col 48}{space 2} .0815209{col 59}{space 1}   -1.24{col 68}{space 3}0.218{col 76}{space 4}-.2623611{col 89}{space 3} .0604232
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1678526{col 48}{space 2} .0779175{col 59}{space 1}   -2.15{col 68}{space 3}0.033{col 76}{space 4}-.3221109{col 89}{space 3}-.0135944
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0032526{col 48}{space 2} .0790354{col 59}{space 1}   -0.04{col 68}{space 3}0.967{col 76}{space 4} -.159724{col 89}{space 3} .1532188
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0410558{col 48}{space 2} .0940399{col 59}{space 1}   -0.44{col 68}{space 3}0.663{col 76}{space 4}-.2272326{col 89}{space 3} .1451209
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0392141{col 48}{space 2} .1188507{col 59}{space 1}    0.33{col 68}{space 3}0.742{col 76}{space 4}-.1960822{col 89}{space 3} .2745104
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} -.049976{col 48}{space 2} .1183155{col 59}{space 1}   -0.42{col 68}{space 3}0.673{col 76}{space 4}-.2842128{col 89}{space 3} .1842607
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5940497{col 48}{space 2} .1011948{col 59}{space 1}    5.87{col 68}{space 3}0.000{col 76}{space 4} .3937078{col 89}{space 3} .7943915
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_authorchild i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       271
{txt}{col 1}Number of PSUs{col 20}= {res}      122{txt}{col 49}Population size{col 67}={res} 267.121285
{txt}{col 49}Design df{col 67}= {res}       121
{txt}{col 49}F({res}  12{txt},{res}    110{txt}){col 67}= {res}      1.57
{txt}{col 49}Prob > F{col 67}= {res}    0.1113
{txt}{col 49}R-squared{col 67}= {res}    0.0731

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.0690424{col 48}{space 2} .0744163{col 59}{space 1}   -0.93{col 68}{space 3}0.355{col 76}{space 4} -.216369{col 89}{space 3} .0782842
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}  .102895{col 48}{space 2} .0406592{col 59}{space 1}    2.53{col 68}{space 3}0.013{col 76}{space 4} .0223995{col 89}{space 3} .1833906
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0004791{col 48}{space 2} .0367121{col 59}{space 1}   -0.01{col 68}{space 3}0.990{col 76}{space 4}-.0731604{col 89}{space 3} .0722021
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4212615{col 48}{space 2} .2994339{col 59}{space 1}    1.41{col 68}{space 3}0.162{col 76}{space 4}-.1715468{col 89}{space 3}  1.01407
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2929628{col 48}{space 2} .3406482{col 59}{space 1}   -0.86{col 68}{space 3}0.391{col 76}{space 4}-.9673657{col 89}{space 3} .3814401
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1479134{col 48}{space 2}  .064853{col 59}{space 1}   -2.28{col 68}{space 3}0.024{col 76}{space 4}-.2763069{col 89}{space 3}-.0195198
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0972703{col 48}{space 2} .0801105{col 59}{space 1}   -1.21{col 68}{space 3}0.227{col 76}{space 4}-.2558703{col 89}{space 3} .0613296
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} -.177376{col 48}{space 2} .0752346{col 59}{space 1}   -2.36{col 68}{space 3}0.020{col 76}{space 4}-.3263228{col 89}{space 3}-.0284293
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0106801{col 48}{space 2} .0805134{col 59}{space 1}   -0.13{col 68}{space 3}0.895{col 76}{space 4}-.1700775{col 89}{space 3} .1487173
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} -.035123{col 48}{space 2} .0963119{col 59}{space 1}   -0.36{col 68}{space 3}0.716{col 76}{space 4}-.2257978{col 89}{space 3} .1555518
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0581427{col 48}{space 2} .1213039{col 59}{space 1}    0.48{col 68}{space 3}0.633{col 76}{space 4}-.1820104{col 89}{space 3} .2982957
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} -.048504{col 48}{space 2} .1178654{col 59}{space 1}   -0.41{col 68}{space 3}0.681{col 76}{space 4}-.2818496{col 89}{space 3} .1848415
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5793909{col 48}{space 2} .0987595{col 59}{space 1}    5.87{col 68}{space 3}0.000{col 76}{space 4} .3838705{col 89}{space 3} .7749113
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_authorchild##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       271
{txt}{col 1}Number of PSUs{col 20}= {res}      122{txt}{col 49}Population size{col 67}={res} 267.121285
{txt}{col 49}Design df{col 67}= {res}       121
{txt}{col 49}F({res}  13{txt},{res}    109{txt}){col 67}= {res}      1.57
{txt}{col 49}Prob > F{col 67}= {res}    0.1058
{txt}{col 49}R-squared{col 67}= {res}    0.0777

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.1121539{col 48}{space 2} .0906356{col 59}{space 1}   -1.24{col 68}{space 3}0.218{col 76}{space 4} -.291591{col 89}{space 3} .0672831
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .0679622{col 48}{space 2} .0536574{col 59}{space 1}    1.27{col 68}{space 3}0.208{col 76}{space 4}-.0382667{col 89}{space 3} .1741911
{txt}{space 34} {c |}
{space 7}treatment1#c.st_authorchild {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1713973{col 48}{space 2} .1430185{col 59}{space 1}    1.20{col 68}{space 3}0.233{col 76}{space 4}-.1117455{col 89}{space 3} .4545402
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0022375{col 48}{space 2} .0362973{col 59}{space 1}   -0.06{col 68}{space 3}0.951{col 76}{space 4}-.0740976{col 89}{space 3} .0696227
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4199125{col 48}{space 2} .2991269{col 59}{space 1}    1.40{col 68}{space 3}0.163{col 76}{space 4} -.172288{col 89}{space 3} 1.012113
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2994846{col 48}{space 2} .3401666{col 59}{space 1}   -0.88{col 68}{space 3}0.380{col 76}{space 4}-.9729341{col 89}{space 3} .3739649
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1505292{col 48}{space 2} .0643505{col 59}{space 1}   -2.34{col 68}{space 3}0.021{col 76}{space 4}-.2779279{col 89}{space 3}-.0231305
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1009117{col 48}{space 2} .0791246{col 59}{space 1}   -1.28{col 68}{space 3}0.205{col 76}{space 4}-.2575599{col 89}{space 3} .0557364
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1755219{col 48}{space 2} .0740207{col 59}{space 1}   -2.37{col 68}{space 3}0.019{col 76}{space 4}-.3220654{col 89}{space 3}-.0289784
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0156779{col 48}{space 2} .0803402{col 59}{space 1}   -0.20{col 68}{space 3}0.846{col 76}{space 4}-.1747325{col 89}{space 3} .1433767
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0397308{col 48}{space 2}  .095882{col 59}{space 1}   -0.41{col 68}{space 3}0.679{col 76}{space 4}-.2295545{col 89}{space 3} .1500928
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0498903{col 48}{space 2}  .120984{col 59}{space 1}    0.41{col 68}{space 3}0.681{col 76}{space 4}-.1896293{col 89}{space 3}   .28941
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0608655{col 48}{space 2} .1205295{col 59}{space 1}   -0.50{col 68}{space 3}0.614{col 76}{space 4}-.2994854{col 89}{space 3} .1777545
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5965771{col 48}{space 2} .1011629{col 59}{space 1}    5.90{col 68}{space 3}0.000{col 76}{space 4} .3962984{col 89}{space 3} .7968557
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. *Interaction model including both ethnocentrism and authoritarianism
. svy: regress st_terrori c.st_ethno##i.treatment1 c.st_authorchild##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  15{txt},{res}    102{txt}){col 67}= {res}      4.59
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1472

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0191412{col 48}{space 2} .0969402{col 59}{space 1}    0.20{col 68}{space 3}0.844{col 76}{space 4}-.1728611{col 89}{space 3} .2111435
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1671138{col 48}{space 2}  .071321{col 59}{space 1}   -2.34{col 68}{space 3}0.021{col 76}{space 4}-.3083741{col 89}{space 3}-.0258535
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .7634099{col 48}{space 2} .1643876{col 59}{space 1}    4.64{col 68}{space 3}0.000{col 76}{space 4} .4378195{col 89}{space 3}    1.089
{txt}{space 34} {c |}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.1198122{col 48}{space 2}  .090811{col 59}{space 1}   -1.32{col 68}{space 3}0.190{col 76}{space 4}-.2996747{col 89}{space 3} .0600504
{txt}{space 34} {c |}
{space 7}treatment1#c.st_authorchild {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.0699534{col 48}{space 2} .1477699{col 59}{space 1}   -0.47{col 68}{space 3}0.637{col 76}{space 4}-.3626303{col 89}{space 3} .2227236
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0102722{col 48}{space 2}  .035883{col 59}{space 1}   -0.29{col 68}{space 3}0.775{col 76}{space 4} -.081343{col 89}{space 3} .0607987
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6778936{col 48}{space 2} .3073803{col 59}{space 1}    2.21{col 68}{space 3}0.029{col 76}{space 4} .0690882{col 89}{space 3} 1.286699
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5973839{col 48}{space 2} .3435271{col 59}{space 1}   -1.74{col 68}{space 3}0.085{col 76}{space 4}-1.277783{col 89}{space 3} .0830149
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1005896{col 48}{space 2} .0624354{col 59}{space 1}   -1.61{col 68}{space 3}0.110{col 76}{space 4}-.2242508{col 89}{space 3} .0230716
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} -.027705{col 48}{space 2} .0791434{col 59}{space 1}   -0.35{col 68}{space 3}0.727{col 76}{space 4}-.1844585{col 89}{space 3} .1290486
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1104627{col 48}{space 2} .0721075{col 59}{space 1}   -1.53{col 68}{space 3}0.128{col 76}{space 4}-.2532808{col 89}{space 3} .0323554
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0896947{col 48}{space 2} .0751877{col 59}{space 1}   -1.19{col 68}{space 3}0.235{col 76}{space 4}-.2386134{col 89}{space 3} .0592239
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1136275{col 48}{space 2} .0899511{col 59}{space 1}   -1.26{col 68}{space 3}0.209{col 76}{space 4} -.291787{col 89}{space 3}  .064532
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0185951{col 48}{space 2}  .119556{col 59}{space 1}   -0.16{col 68}{space 3}0.877{col 76}{space 4}-.2553908{col 89}{space 3} .2182006
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0808321{col 48}{space 2}  .124065{col 59}{space 1}   -0.65{col 68}{space 3}0.516{col 76}{space 4}-.3265585{col 89}{space 3} .1648942
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5684138{col 48}{space 2} .1081742{col 59}{space 1}    5.25{col 68}{space 3}0.000{col 76}{space 4}  .354161{col 89}{space 3} .7826665
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. 
. ********************************************************************************
. *L2 The activation effect on specific civil liberties measures
. ********************************************************************************
. 
. 
. 
.                                                                         *Table L2*
.                 ***The activation effect on specific civil liberties measures***
.                                                                         
.                                                                         
. /*" "Suppose the government suspected that a terrorist act was about to happen. 
> Do you think the authorities should have the right to...detain people for as 
> long as they want without putting them on trial?"*/
. svy: regress st_terror1 c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       259
{txt}{col 1}Number of PSUs{col 20}= {res}      119{txt}{col 49}Population size{col 67}={res} 257.218702
{txt}{col 49}Design df{col 67}= {res}       118
{txt}{col 49}F({res}  11{txt},{res}    108{txt}){col 67}= {res}      3.40
{txt}{col 49}Prob > F{col 67}= {res}    0.0005
{txt}{col 49}R-squared{col 67}= {res}    0.1039

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terror1{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2267109{col 48}{space 2} .1252432{col 59}{space 1}    1.81{col 68}{space 3}0.073{col 76}{space 4}-.0213047{col 89}{space 3} .4747265
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0307866{col 48}{space 2} .0467564{col 59}{space 1}    0.66{col 68}{space 3}0.512{col 76}{space 4}-.0618038{col 89}{space 3}  .123377
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4396113{col 48}{space 2} .3782239{col 59}{space 1}    1.16{col 68}{space 3}0.247{col 76}{space 4}-.3093751{col 89}{space 3} 1.188598
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.1257662{col 48}{space 2} .4430262{col 59}{space 1}   -0.28{col 68}{space 3}0.777{col 76}{space 4}-1.003079{col 89}{space 3} .7515463
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1743198{col 48}{space 2} .0703197{col 59}{space 1}   -2.48{col 68}{space 3}0.015{col 76}{space 4} -.313572{col 89}{space 3}-.0350677
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1864729{col 48}{space 2} .0858784{col 59}{space 1}   -2.17{col 68}{space 3}0.032{col 76}{space 4}-.3565356{col 89}{space 3}-.0164103
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.2846809{col 48}{space 2} .0833354{col 59}{space 1}   -3.42{col 68}{space 3}0.001{col 76}{space 4}-.4497078{col 89}{space 3}-.1196541
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0242647{col 48}{space 2} .0869931{col 59}{space 1}   -0.28{col 68}{space 3}0.781{col 76}{space 4}-.1965348{col 89}{space 3} .1480054
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1233891{col 48}{space 2} .1040567{col 59}{space 1}   -1.19{col 68}{space 3}0.238{col 76}{space 4}-.3294497{col 89}{space 3} .0826715
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0658448{col 48}{space 2}  .148174{col 59}{space 1}    0.44{col 68}{space 3}0.658{col 76}{space 4}  -.22758{col 89}{space 3} .3592697
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0456194{col 48}{space 2} .1152797{col 59}{space 1}    0.40{col 68}{space 3}0.693{col 76}{space 4}-.1826657{col 89}{space 3} .2739045
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .4025844{col 48}{space 2} .1148449{col 59}{space 1}    3.51{col 68}{space 3}0.001{col 76}{space 4} .1751603{col 89}{space 3} .6300085
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terror1 c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       259
{txt}{col 1}Number of PSUs{col 20}= {res}      119{txt}{col 49}Population size{col 67}={res} 257.218702
{txt}{col 49}Design df{col 67}= {res}       118
{txt}{col 49}F({res}  12{txt},{res}    107{txt}){col 67}= {res}      3.41
{txt}{col 49}Prob > F{col 67}= {res}    0.0003
{txt}{col 49}R-squared{col 67}= {res}    0.1188

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terror1{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2164701{col 48}{space 2} .1225399{col 59}{space 1}    1.77{col 68}{space 3}0.080{col 76}{space 4}-.0261923{col 89}{space 3} .4591326
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1067115{col 48}{space 2}  .050218{col 59}{space 1}    2.12{col 68}{space 3}0.036{col 76}{space 4} .0072663{col 89}{space 3} .2061567
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0235615{col 48}{space 2} .0465487{col 59}{space 1}    0.51{col 68}{space 3}0.614{col 76}{space 4}-.0686175{col 89}{space 3} .1157406
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4663036{col 48}{space 2} .3736716{col 59}{space 1}    1.25{col 68}{space 3}0.215{col 76}{space 4}-.2736678{col 89}{space 3} 1.206275
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} -.170163{col 48}{space 2} .4367261{col 59}{space 1}   -0.39{col 68}{space 3}0.698{col 76}{space 4}   -1.035{col 89}{space 3} .6946736
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1679946{col 48}{space 2} .0648306{col 59}{space 1}   -2.59{col 68}{space 3}0.011{col 76}{space 4}-.2963767{col 89}{space 3}-.0396124
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} -.178796{col 48}{space 2} .0818817{col 59}{space 1}   -2.18{col 68}{space 3}0.031{col 76}{space 4}-.3409441{col 89}{space 3}-.0166479
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.2924853{col 48}{space 2} .0792053{col 59}{space 1}   -3.69{col 68}{space 3}0.000{col 76}{space 4}-.4493334{col 89}{space 3}-.1356372
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0378688{col 48}{space 2} .0894765{col 59}{space 1}   -0.42{col 68}{space 3}0.673{col 76}{space 4}-.2150567{col 89}{space 3} .1393191
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1221238{col 48}{space 2} .1077182{col 59}{space 1}   -1.13{col 68}{space 3}0.259{col 76}{space 4}-.3354351{col 89}{space 3} .0911875
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0803833{col 48}{space 2} .1513066{col 59}{space 1}    0.53{col 68}{space 3}0.596{col 76}{space 4} -.219245{col 89}{space 3} .3800117
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0416025{col 48}{space 2} .1161896{col 59}{space 1}    0.36{col 68}{space 3}0.721{col 76}{space 4}-.1884847{col 89}{space 3} .2716896
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .3924859{col 48}{space 2} .1129008{col 59}{space 1}    3.48{col 68}{space 3}0.001{col 76}{space 4} .1689117{col 89}{space 3} .6160602
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terror1 c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       259
{txt}{col 1}Number of PSUs{col 20}= {res}      119{txt}{col 49}Population size{col 67}={res} 257.218702
{txt}{col 49}Design df{col 67}= {res}       118
{txt}{col 49}F({res}  13{txt},{res}    106{txt}){col 67}= {res}      6.01
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1528

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terror1{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0556128{col 48}{space 2} .1376076{col 59}{space 1}    0.40{col 68}{space 3}0.687{col 76}{space 4}-.2168877{col 89}{space 3} .3281132
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} -.203928{col 48}{space 2} .0973804{col 59}{space 1}   -2.09{col 68}{space 3}0.038{col 76}{space 4}-.3967678{col 89}{space 3}-.0110882
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .7913793{col 48}{space 2} .2191318{col 59}{space 1}    3.61{col 68}{space 3}0.000{col 76}{space 4} .3574388{col 89}{space 3}  1.22532
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0285458{col 48}{space 2} .0448697{col 59}{space 1}    0.64{col 68}{space 3}0.526{col 76}{space 4}-.0603084{col 89}{space 3}    .1174
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .570619{col 48}{space 2} .3667219{col 59}{space 1}    1.56{col 68}{space 3}0.122{col 76}{space 4}-.1555902{col 89}{space 3} 1.296828
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2886275{col 48}{space 2} .4321823{col 59}{space 1}   -0.67{col 68}{space 3}0.506{col 76}{space 4}-1.144466{col 89}{space 3} .5672111
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1452734{col 48}{space 2}  .063742{col 59}{space 1}   -2.28{col 68}{space 3}0.024{col 76}{space 4}-.2714999{col 89}{space 3} -.019047
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1466402{col 48}{space 2} .0810729{col 59}{space 1}   -1.81{col 68}{space 3}0.073{col 76}{space 4}-.3071865{col 89}{space 3} .0139062
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.2612032{col 48}{space 2} .0764235{col 59}{space 1}   -3.42{col 68}{space 3}0.001{col 76}{space 4}-.4125425{col 89}{space 3}-.1098639
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0686079{col 48}{space 2} .0826483{col 59}{space 1}   -0.83{col 68}{space 3}0.408{col 76}{space 4}-.2322742{col 89}{space 3} .0950583
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} -.157929{col 48}{space 2} .1007952{col 59}{space 1}   -1.57{col 68}{space 3}0.120{col 76}{space 4}-.3575309{col 89}{space 3} .0416729
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0463501{col 48}{space 2} .1450768{col 59}{space 1}    0.32{col 68}{space 3}0.750{col 76}{space 4}-.2409414{col 89}{space 3} .3336417
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0096707{col 48}{space 2} .1131811{col 59}{space 1}    0.09{col 68}{space 3}0.932{col 76}{space 4}-.2144588{col 89}{space 3} .2338001
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .441065{col 48}{space 2} .1099698{col 59}{space 1}    4.01{col 68}{space 3}0.000{col 76}{space 4}  .223295{col 89}{space 3} .6588351
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. *"... tap people’s telephone conversations?"
. svy: regress st_terror2 c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       263
{txt}{col 1}Number of PSUs{col 20}= {res}      119{txt}{col 49}Population size{col 67}={res} 260.052578
{txt}{col 49}Design df{col 67}= {res}       118
{txt}{col 49}F({res}  11{txt},{res}    108{txt}){col 67}= {res}      1.61
{txt}{col 49}Prob > F{col 67}= {res}    0.1062
{txt}{col 49}R-squared{col 67}= {res}    0.0767

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terror2{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0736873{col 48}{space 2} .0982129{col 59}{space 1}    0.75{col 68}{space 3}0.455{col 76}{space 4}-.1208009{col 89}{space 3} .2681756
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0228785{col 48}{space 2} .0374656{col 59}{space 1}   -0.61{col 68}{space 3}0.543{col 76}{space 4}-.0970706{col 89}{space 3} .0513135
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7914041{col 48}{space 2} .3832581{col 59}{space 1}    2.06{col 68}{space 3}0.041{col 76}{space 4} .0324487{col 89}{space 3}  1.55036
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6879958{col 48}{space 2}  .450202{col 59}{space 1}   -1.53{col 68}{space 3}0.129{col 76}{space 4}-1.579518{col 89}{space 3} .2035267
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0638818{col 48}{space 2} .0787833{col 59}{space 1}   -0.81{col 68}{space 3}0.419{col 76}{space 4}-.2198943{col 89}{space 3} .0921307
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0294351{col 48}{space 2} .0854689{col 59}{space 1}    0.34{col 68}{space 3}0.731{col 76}{space 4}-.1398166{col 89}{space 3} .1986867
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}  -.03675{col 48}{space 2} .0866869{col 59}{space 1}   -0.42{col 68}{space 3}0.672{col 76}{space 4}-.2084138{col 89}{space 3} .1349137
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0776187{col 48}{space 2}  .081486{col 59}{space 1}   -0.95{col 68}{space 3}0.343{col 76}{space 4}-.2389831{col 89}{space 3} .0837457
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1244411{col 48}{space 2} .1045468{col 59}{space 1}   -1.19{col 68}{space 3}0.236{col 76}{space 4}-.3314722{col 89}{space 3}   .08259
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0281334{col 48}{space 2} .1303791{col 59}{space 1}   -0.22{col 68}{space 3}0.830{col 76}{space 4}-.2863194{col 89}{space 3} .2300526
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1584633{col 48}{space 2} .1317583{col 59}{space 1}   -1.20{col 68}{space 3}0.232{col 76}{space 4}-.4193806{col 89}{space 3} .1024539
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6101261{col 48}{space 2} .1259381{col 59}{space 1}    4.84{col 68}{space 3}0.000{col 76}{space 4} .3607344{col 89}{space 3} .8595177
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terror2 c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       263
{txt}{col 1}Number of PSUs{col 20}= {res}      119{txt}{col 49}Population size{col 67}={res} 260.052578
{txt}{col 49}Design df{col 67}= {res}       118
{txt}{col 49}F({res}  12{txt},{res}    107{txt}){col 67}= {res}      1.72
{txt}{col 49}Prob > F{col 67}= {res}    0.0724
{txt}{col 49}R-squared{col 67}= {res}    0.1003

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terror2{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0635476{col 48}{space 2} .0964933{col 59}{space 1}    0.66{col 68}{space 3}0.511{col 76}{space 4}-.1275355{col 89}{space 3} .2546307
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1142206{col 48}{space 2} .0435756{col 59}{space 1}    2.62{col 68}{space 3}0.010{col 76}{space 4}  .027929{col 89}{space 3} .2005123
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}  -.02948{col 48}{space 2} .0382118{col 59}{space 1}   -0.77{col 68}{space 3}0.442{col 76}{space 4}-.1051497{col 89}{space 3} .0461897
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .8273447{col 48}{space 2} .3740014{col 59}{space 1}    2.21{col 68}{space 3}0.029{col 76}{space 4} .0867202{col 89}{space 3} 1.567969
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.7470877{col 48}{space 2} .4375443{col 59}{space 1}   -1.71{col 68}{space 3}0.090{col 76}{space 4}-1.613544{col 89}{space 3} .1193692
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0584269{col 48}{space 2}  .074095{col 59}{space 1}   -0.79{col 68}{space 3}0.432{col 76}{space 4}-.2051552{col 89}{space 3} .0883014
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}  .037973{col 48}{space 2} .0833781{col 59}{space 1}    0.46{col 68}{space 3}0.650{col 76}{space 4}-.1271383{col 89}{space 3} .2030844
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0440786{col 48}{space 2} .0830773{col 59}{space 1}   -0.53{col 68}{space 3}0.597{col 76}{space 4}-.2085944{col 89}{space 3} .1204371
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} -.091437{col 48}{space 2} .0858936{col 59}{space 1}   -1.06{col 68}{space 3}0.289{col 76}{space 4}-.2615298{col 89}{space 3} .0786558
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1233903{col 48}{space 2} .1088159{col 59}{space 1}   -1.13{col 68}{space 3}0.259{col 76}{space 4}-.3388754{col 89}{space 3} .0920949
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} -.012573{col 48}{space 2} .1339953{col 59}{space 1}   -0.09{col 68}{space 3}0.925{col 76}{space 4}-.2779203{col 89}{space 3} .2527743
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1642363{col 48}{space 2} .1326967{col 59}{space 1}   -1.24{col 68}{space 3}0.218{col 76}{space 4}-.4270119{col 89}{space 3} .0985394
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5980385{col 48}{space 2} .1248746{col 59}{space 1}    4.79{col 68}{space 3}0.000{col 76}{space 4} .3507529{col 89}{space 3} .8453241
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terror2 c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       263
{txt}{col 1}Number of PSUs{col 20}= {res}      119{txt}{col 49}Population size{col 67}={res} 260.052578
{txt}{col 49}Design df{col 67}= {res}       118
{txt}{col 49}F({res}  13{txt},{res}    106{txt}){col 67}= {res}      3.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0006
{txt}{col 49}R-squared{col 67}= {res}    0.1179

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terror2{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2}-.0353985{col 48}{space 2} .1107889{col 59}{space 1}   -0.32{col 68}{space 3}0.750{col 76}{space 4}-.2547907{col 89}{space 3} .1839936
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.0782732{col 48}{space 2} .0785142{col 59}{space 1}   -1.00{col 68}{space 3}0.321{col 76}{space 4}-.2337526{col 89}{space 3} .0772062
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .4885649{col 48}{space 2} .1580005{col 59}{space 1}    3.09{col 68}{space 3}0.002{col 76}{space 4}  .175681{col 89}{space 3} .8014489
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0262526{col 48}{space 2}   .03776{col 59}{space 1}   -0.70{col 68}{space 3}0.488{col 76}{space 4}-.1010276{col 89}{space 3} .0485224
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .8952095{col 48}{space 2} .3653042{col 59}{space 1}    2.45{col 68}{space 3}0.016{col 76}{space 4} .1718077{col 89}{space 3} 1.618611
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.8252736{col 48}{space 2} .4351333{col 59}{space 1}   -1.90{col 68}{space 3}0.060{col 76}{space 4}-1.686956{col 89}{space 3} .0364087
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0445391{col 48}{space 2} .0725514{col 59}{space 1}   -0.61{col 68}{space 3}0.540{col 76}{space 4}-.1882106{col 89}{space 3} .0991324
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0569875{col 48}{space 2} .0819696{col 59}{space 1}    0.70{col 68}{space 3}0.488{col 76}{space 4}-.1053345{col 89}{space 3} .2193096
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0242836{col 48}{space 2} .0803855{col 59}{space 1}   -0.30{col 68}{space 3}0.763{col 76}{space 4}-.1834687{col 89}{space 3} .1349015
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.1106615{col 48}{space 2} .0774047{col 59}{space 1}   -1.43{col 68}{space 3}0.155{col 76}{space 4}-.2639439{col 89}{space 3} .0426208
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1454335{col 48}{space 2} .1011012{col 59}{space 1}   -1.44{col 68}{space 3}0.153{col 76}{space 4}-.3456415{col 89}{space 3} .0547745
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0337811{col 48}{space 2} .1282647{col 59}{space 1}   -0.26{col 68}{space 3}0.793{col 76}{space 4}-.2877802{col 89}{space 3} .2202179
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1814033{col 48}{space 2}  .130072{col 59}{space 1}   -1.39{col 68}{space 3}0.166{col 76}{space 4}-.4389813{col 89}{space 3} .0761746
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6275438{col 48}{space 2} .1225756{col 59}{space 1}    5.12{col 68}{space 3}0.000{col 76}{space 4} .3848107{col 89}{space 3} .8702768
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. *"... stop and search people in the street at random?"
. svy: regress st_terror3 c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       261
{txt}{col 1}Number of PSUs{col 20}= {res}      118{txt}{col 49}Population size{col 67}={res} 257.584925
{txt}{col 49}Design df{col 67}= {res}       117
{txt}{col 49}F({res}  11{txt},{res}    107{txt}){col 67}= {res}      1.35
{txt}{col 49}Prob > F{col 67}= {res}    0.2069
{txt}{col 49}R-squared{col 67}= {res}    0.0474

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terror3{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1580402{col 48}{space 2} .1096936{col 59}{space 1}    1.44{col 68}{space 3}0.152{col 76}{space 4}-.0592022{col 89}{space 3} .3752826
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0197606{col 48}{space 2} .0451627{col 59}{space 1}   -0.44{col 68}{space 3}0.663{col 76}{space 4} -.109203{col 89}{space 3} .0696817
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}   .50987{col 48}{space 2} .3879033{col 59}{space 1}    1.31{col 68}{space 3}0.191{col 76}{space 4}-.2583523{col 89}{space 3} 1.278092
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.7178923{col 48}{space 2} .4432569{col 59}{space 1}   -1.62{col 68}{space 3}0.108{col 76}{space 4}-1.595739{col 89}{space 3} .1599548
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1177966{col 48}{space 2} .0846922{col 59}{space 1}   -1.39{col 68}{space 3}0.167{col 76}{space 4}-.2855251{col 89}{space 3}  .049932
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0316258{col 48}{space 2} .0954703{col 59}{space 1}   -0.33{col 68}{space 3}0.741{col 76}{space 4}-.2206997{col 89}{space 3} .1574481
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0607776{col 48}{space 2} .0936653{col 59}{space 1}   -0.65{col 68}{space 3}0.518{col 76}{space 4}-.2462768{col 89}{space 3} .1247217
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0300575{col 48}{space 2} .0907109{col 59}{space 1}   -0.33{col 68}{space 3}0.741{col 76}{space 4}-.2097057{col 89}{space 3} .1495907
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0443969{col 48}{space 2} .1086665{col 59}{space 1}    0.41{col 68}{space 3}0.684{col 76}{space 4}-.1708115{col 89}{space 3} .2596053
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0215657{col 48}{space 2} .1479383{col 59}{space 1}   -0.15{col 68}{space 3}0.884{col 76}{space 4}-.3145496{col 89}{space 3} .2714183
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0945775{col 48}{space 2} .1460171{col 59}{space 1}   -0.65{col 68}{space 3}0.518{col 76}{space 4}-.3837566{col 89}{space 3} .1946017
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5395174{col 48}{space 2} .1288132{col 59}{space 1}    4.19{col 68}{space 3}0.000{col 76}{space 4} .2844097{col 89}{space 3} .7946251
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terror3 c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       261
{txt}{col 1}Number of PSUs{col 20}= {res}      118{txt}{col 49}Population size{col 67}={res} 257.584925
{txt}{col 49}Design df{col 67}= {res}       117
{txt}{col 49}F({res}  12{txt},{res}    106{txt}){col 67}= {res}      2.07
{txt}{col 49}Prob > F{col 67}= {res}    0.0250
{txt}{col 49}R-squared{col 67}= {res}    0.0699

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terror3{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1476746{col 48}{space 2} .1110092{col 59}{space 1}    1.33{col 68}{space 3}0.186{col 76}{space 4}-.0721732{col 89}{space 3} .3675224
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1176109{col 48}{space 2}  .041931{col 59}{space 1}    2.80{col 68}{space 3}0.006{col 76}{space 4} .0345687{col 89}{space 3} .2006531
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0275644{col 48}{space 2} .0454956{col 59}{space 1}   -0.61{col 68}{space 3}0.546{col 76}{space 4} -.117666{col 89}{space 3} .0625371
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5456052{col 48}{space 2} .3834166{col 59}{space 1}    1.42{col 68}{space 3}0.157{col 76}{space 4}-.2137313{col 89}{space 3} 1.304942
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.7775386{col 48}{space 2} .4353509{col 59}{space 1}   -1.79{col 68}{space 3}0.077{col 76}{space 4}-1.639728{col 89}{space 3} .0846511
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1120877{col 48}{space 2} .0805415{col 59}{space 1}   -1.39{col 68}{space 3}0.167{col 76}{space 4}-.2715958{col 89}{space 3} .0474205
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0237494{col 48}{space 2} .0941187{col 59}{space 1}   -0.25{col 68}{space 3}0.801{col 76}{space 4}-.2101466{col 89}{space 3} .1626479
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0694133{col 48}{space 2} .0919006{col 59}{space 1}   -0.76{col 68}{space 3}0.452{col 76}{space 4}-.2514177{col 89}{space 3} .1125911
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0444517{col 48}{space 2} .0956271{col 59}{space 1}   -0.46{col 68}{space 3}0.643{col 76}{space 4}-.2338362{col 89}{space 3} .1449327
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0456274{col 48}{space 2} .1139255{col 59}{space 1}    0.40{col 68}{space 3}0.690{col 76}{space 4}-.1799961{col 89}{space 3} .2712509
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0051398{col 48}{space 2} .1519415{col 59}{space 1}   -0.03{col 68}{space 3}0.973{col 76}{space 4}-.3060521{col 89}{space 3} .2957725
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} -.101169{col 48}{space 2} .1465272{col 59}{space 1}   -0.69{col 68}{space 3}0.491{col 76}{space 4}-.3913585{col 89}{space 3} .1890206
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5279899{col 48}{space 2} .1283512{col 59}{space 1}    4.11{col 68}{space 3}0.000{col 76}{space 4} .2737971{col 89}{space 3} .7821826
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terror3 c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       261
{txt}{col 1}Number of PSUs{col 20}= {res}      118{txt}{col 49}Population size{col 67}={res} 257.584925
{txt}{col 49}Design df{col 67}= {res}       117
{txt}{col 49}F({res}  13{txt},{res}    105{txt}){col 67}= {res}      3.12
{txt}{col 49}Prob > F{col 67}= {res}    0.0006
{txt}{col 49}R-squared{col 67}= {res}    0.1026

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terror3{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0033482{col 48}{space 2} .1162618{col 59}{space 1}    0.03{col 68}{space 3}0.977{col 76}{space 4}-.2269021{col 89}{space 3} .2335986
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} -.160398{col 48}{space 2} .0816353{col 59}{space 1}   -1.96{col 68}{space 3}0.052{col 76}{space 4}-.3220724{col 89}{space 3} .0012765
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .7044101{col 48}{space 2} .1880711{col 59}{space 1}    3.75{col 68}{space 3}0.000{col 76}{space 4} .3319452{col 89}{space 3} 1.076875
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0225395{col 48}{space 2} .0444422{col 59}{space 1}   -0.51{col 68}{space 3}0.613{col 76}{space 4}-.1105549{col 89}{space 3} .0654759
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6426262{col 48}{space 2} .3677515{col 59}{space 1}    1.75{col 68}{space 3}0.083{col 76}{space 4}-.0856864{col 89}{space 3} 1.370939
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.8884144{col 48}{space 2} .4296932{col 59}{space 1}   -2.07{col 68}{space 3}0.041{col 76}{space 4}-1.739399{col 89}{space 3}-.0374296
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0921533{col 48}{space 2} .0793357{col 59}{space 1}   -1.16{col 68}{space 3}0.248{col 76}{space 4}-.2492735{col 89}{space 3} .0649669
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0053195{col 48}{space 2} .0932691{col 59}{space 1}    0.06{col 68}{space 3}0.955{col 76}{space 4}-.1793951{col 89}{space 3} .1900342
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0404274{col 48}{space 2}  .089845{col 59}{space 1}   -0.45{col 68}{space 3}0.654{col 76}{space 4}-.2183606{col 89}{space 3} .1375058
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0721201{col 48}{space 2} .0848741{col 59}{space 1}   -0.85{col 68}{space 3}0.397{col 76}{space 4}-.2402089{col 89}{space 3} .0959686
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0134484{col 48}{space 2} .1043149{col 59}{space 1}    0.13{col 68}{space 3}0.898{col 76}{space 4}-.1931418{col 89}{space 3} .2200387
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0358528{col 48}{space 2} .1480363{col 59}{space 1}   -0.24{col 68}{space 3}0.809{col 76}{space 4} -.329031{col 89}{space 3} .2573253
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1231063{col 48}{space 2} .1420815{col 59}{space 1}   -0.87{col 68}{space 3}0.388{col 76}{space 4}-.4044913{col 89}{space 3} .1582787
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5709417{col 48}{space 2} .1242788{col 59}{space 1}    4.59{col 68}{space 3}0.000{col 76}{space 4}  .324814{col 89}{space 3} .8170694
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. 
. ********************************************************************************
. *L3 The activation effect for different levels of ethnocentrism
. ********************************************************************************
. 
.                                                                         *Figure L1*
.                 ***The activation effect for different levels of ethnocentrism***
. 
.                 
. *Calculation of margins for different percentiles
. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      4.88
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1330

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0007221{col 48}{space 2} .0977606{col 59}{space 1}    0.01{col 68}{space 3}0.994{col 76}{space 4}-.1929051{col 89}{space 3} .1943494
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1518117{col 48}{space 2}  .070707{col 59}{space 1}   -2.15{col 68}{space 3}0.034{col 76}{space 4}-.2918558{col 89}{space 3}-.0117676
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6750141{col 48}{space 2} .1507665{col 59}{space 1}    4.48{col 68}{space 3}0.000{col 76}{space 4}  .376402{col 89}{space 3} .9736263
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0061306{col 48}{space 2} .0367219{col 59}{space 1}   -0.17{col 68}{space 3}0.868{col 76}{space 4}-.0788629{col 89}{space 3} .0666017
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7003869{col 48}{space 2} .3186851{col 59}{space 1}    2.20{col 68}{space 3}0.030{col 76}{space 4} .0691909{col 89}{space 3} 1.331583
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6554761{col 48}{space 2} .3602407{col 59}{space 1}   -1.82{col 68}{space 3}0.071{col 76}{space 4}-1.368978{col 89}{space 3} .0580259
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0975267{col 48}{space 2} .0645064{col 59}{space 1}   -1.51{col 68}{space 3}0.133{col 76}{space 4}-.2252897{col 89}{space 3} .0302363
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}    -.022{col 48}{space 2} .0792046{col 59}{space 1}   -0.28{col 68}{space 3}0.782{col 76}{space 4}-.1788748{col 89}{space 3} .1348747
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1095112{col 48}{space 2} .0748847{col 59}{space 1}   -1.46{col 68}{space 3}0.146{col 76}{space 4}-.2578299{col 89}{space 3} .0388074
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0841434{col 48}{space 2} .0728254{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2283832{col 89}{space 3} .0600964
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1028858{col 48}{space 2} .0890782{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2793165{col 89}{space 3} .0735448
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0070997{col 48}{space 2} .1194672{col 59}{space 1}   -0.06{col 68}{space 3}0.953{col 76}{space 4}-.2437196{col 89}{space 3} .2295203
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1023694{col 48}{space 2} .1167093{col 59}{space 1}   -0.88{col 68}{space 3}0.382{col 76}{space 4}-.3335268{col 89}{space 3}  .128788
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5497256{col 48}{space 2}    .1046{col 59}{space 1}    5.26{col 68}{space 3}0.000{col 76}{space 4} .3425521{col 89}{space 3} .7568991
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. sum st_ethno if e(sample), detail

                          {txt}st_ethno
{hline 61}
      Percentiles      Smallest
 1%    {res}      .05              0
{txt} 5%    {res} .1166667       .0333333
{txt}10%    {res} .1583333            .05       {txt}Obs         {res}        254
{txt}25%    {res}      .25       .0583333       {txt}Sum of Wgt. {res}        254

{txt}50%    {res} .3833333                      {txt}Mean          {res}  .397605
                        {txt}Largest       Std. Dev.     {res} .1995411
{txt}75%    {res} .5333334             .9
{txt}90%    {res}      .65             .9       {txt}Variance      {res} .0398166
{txt}95%    {res}     .775       .9166667       {txt}Skewness      {res} .5145561
{txt}99%    {res}       .9              1       {txt}Kurtosis      {res}  2.86251
{txt}
{com}. tab respid if e(sample) & st_ethno>0.3833333

{txt}IDENTIFIKAT {c |}
 IONSNUMMER {c |}
        DES {c |}
  BEFRAGTEN {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          9 {c |}{res}          1        0.77        0.77
{txt}         72 {c |}{res}          1        0.77        1.54
{txt}         96 {c |}{res}          1        0.77        2.31
{txt}        116 {c |}{res}          1        0.77        3.08
{txt}        132 {c |}{res}          1        0.77        3.85
{txt}        149 {c |}{res}          1        0.77        4.62
{txt}        214 {c |}{res}          1        0.77        5.38
{txt}        286 {c |}{res}          1        0.77        6.15
{txt}        288 {c |}{res}          1        0.77        6.92
{txt}        308 {c |}{res}          1        0.77        7.69
{txt}        335 {c |}{res}          1        0.77        8.46
{txt}        348 {c |}{res}          1        0.77        9.23
{txt}        372 {c |}{res}          1        0.77       10.00
{txt}        380 {c |}{res}          1        0.77       10.77
{txt}        493 {c |}{res}          1        0.77       11.54
{txt}        583 {c |}{res}          1        0.77       12.31
{txt}        609 {c |}{res}          1        0.77       13.08
{txt}        672 {c |}{res}          1        0.77       13.85
{txt}        673 {c |}{res}          1        0.77       14.62
{txt}        685 {c |}{res}          1        0.77       15.38
{txt}        695 {c |}{res}          1        0.77       16.15
{txt}        759 {c |}{res}          1        0.77       16.92
{txt}        770 {c |}{res}          1        0.77       17.69
{txt}        825 {c |}{res}          1        0.77       18.46
{txt}        839 {c |}{res}          1        0.77       19.23
{txt}        897 {c |}{res}          1        0.77       20.00
{txt}        944 {c |}{res}          1        0.77       20.77
{txt}        979 {c |}{res}          1        0.77       21.54
{txt}       1005 {c |}{res}          1        0.77       22.31
{txt}       1061 {c |}{res}          1        0.77       23.08
{txt}       1100 {c |}{res}          1        0.77       23.85
{txt}       1123 {c |}{res}          1        0.77       24.62
{txt}       1199 {c |}{res}          1        0.77       25.38
{txt}       1241 {c |}{res}          1        0.77       26.15
{txt}       1301 {c |}{res}          1        0.77       26.92
{txt}       1302 {c |}{res}          1        0.77       27.69
{txt}       1327 {c |}{res}          1        0.77       28.46
{txt}       1331 {c |}{res}          1        0.77       29.23
{txt}       1333 {c |}{res}          1        0.77       30.00
{txt}       1401 {c |}{res}          1        0.77       30.77
{txt}       1496 {c |}{res}          1        0.77       31.54
{txt}       1517 {c |}{res}          1        0.77       32.31
{txt}       1553 {c |}{res}          1        0.77       33.08
{txt}       1559 {c |}{res}          1        0.77       33.85
{txt}       1575 {c |}{res}          1        0.77       34.62
{txt}       1636 {c |}{res}          1        0.77       35.38
{txt}       1666 {c |}{res}          1        0.77       36.15
{txt}       1677 {c |}{res}          1        0.77       36.92
{txt}       1709 {c |}{res}          1        0.77       37.69
{txt}       1716 {c |}{res}          1        0.77       38.46
{txt}       1733 {c |}{res}          1        0.77       39.23
{txt}       1743 {c |}{res}          1        0.77       40.00
{txt}       1744 {c |}{res}          1        0.77       40.77
{txt}       1778 {c |}{res}          1        0.77       41.54
{txt}       1796 {c |}{res}          1        0.77       42.31
{txt}       1803 {c |}{res}          1        0.77       43.08
{txt}       1806 {c |}{res}          1        0.77       43.85
{txt}       1809 {c |}{res}          1        0.77       44.62
{txt}       1821 {c |}{res}          1        0.77       45.38
{txt}       1846 {c |}{res}          1        0.77       46.15
{txt}       1851 {c |}{res}          1        0.77       46.92
{txt}       1853 {c |}{res}          1        0.77       47.69
{txt}       1921 {c |}{res}          1        0.77       48.46
{txt}       1942 {c |}{res}          1        0.77       49.23
{txt}       1968 {c |}{res}          1        0.77       50.00
{txt}       2009 {c |}{res}          1        0.77       50.77
{txt}       2013 {c |}{res}          1        0.77       51.54
{txt}       2055 {c |}{res}          1        0.77       52.31
{txt}       2092 {c |}{res}          1        0.77       53.08
{txt}       2104 {c |}{res}          1        0.77       53.85
{txt}       2157 {c |}{res}          1        0.77       54.62
{txt}       2174 {c |}{res}          1        0.77       55.38
{txt}       2196 {c |}{res}          1        0.77       56.15
{txt}       2201 {c |}{res}          1        0.77       56.92
{txt}       2247 {c |}{res}          1        0.77       57.69
{txt}       2261 {c |}{res}          1        0.77       58.46
{txt}       2339 {c |}{res}          1        0.77       59.23
{txt}       2344 {c |}{res}          1        0.77       60.00
{txt}       2357 {c |}{res}          1        0.77       60.77
{txt}       2369 {c |}{res}          1        0.77       61.54
{txt}       2428 {c |}{res}          1        0.77       62.31
{txt}       2440 {c |}{res}          1        0.77       63.08
{txt}       2449 {c |}{res}          1        0.77       63.85
{txt}       2451 {c |}{res}          1        0.77       64.62
{txt}       2468 {c |}{res}          1        0.77       65.38
{txt}       2486 {c |}{res}          1        0.77       66.15
{txt}       2490 {c |}{res}          1        0.77       66.92
{txt}       2504 {c |}{res}          1        0.77       67.69
{txt}       2517 {c |}{res}          1        0.77       68.46
{txt}       2519 {c |}{res}          1        0.77       69.23
{txt}       2528 {c |}{res}          1        0.77       70.00
{txt}       2575 {c |}{res}          1        0.77       70.77
{txt}       2582 {c |}{res}          1        0.77       71.54
{txt}       2587 {c |}{res}          1        0.77       72.31
{txt}       2607 {c |}{res}          1        0.77       73.08
{txt}       2617 {c |}{res}          1        0.77       73.85
{txt}       2621 {c |}{res}          1        0.77       74.62
{txt}       2626 {c |}{res}          1        0.77       75.38
{txt}       2631 {c |}{res}          1        0.77       76.15
{txt}       2642 {c |}{res}          1        0.77       76.92
{txt}       2682 {c |}{res}          1        0.77       77.69
{txt}       2711 {c |}{res}          1        0.77       78.46
{txt}       2755 {c |}{res}          1        0.77       79.23
{txt}       2757 {c |}{res}          1        0.77       80.00
{txt}       2812 {c |}{res}          1        0.77       80.77
{txt}       2832 {c |}{res}          1        0.77       81.54
{txt}       2841 {c |}{res}          1        0.77       82.31
{txt}       2852 {c |}{res}          1        0.77       83.08
{txt}       2865 {c |}{res}          1        0.77       83.85
{txt}       2880 {c |}{res}          1        0.77       84.62
{txt}       2903 {c |}{res}          1        0.77       85.38
{txt}       2919 {c |}{res}          1        0.77       86.15
{txt}       3009 {c |}{res}          1        0.77       86.92
{txt}       3062 {c |}{res}          1        0.77       87.69
{txt}       3079 {c |}{res}          1        0.77       88.46
{txt}       3088 {c |}{res}          1        0.77       89.23
{txt}       3093 {c |}{res}          1        0.77       90.00
{txt}       3097 {c |}{res}          1        0.77       90.77
{txt}       3140 {c |}{res}          1        0.77       91.54
{txt}       3178 {c |}{res}          1        0.77       92.31
{txt}       3206 {c |}{res}          1        0.77       93.08
{txt}       3245 {c |}{res}          1        0.77       93.85
{txt}       3291 {c |}{res}          1        0.77       94.62
{txt}       3308 {c |}{res}          1        0.77       95.38
{txt}       3316 {c |}{res}          1        0.77       96.15
{txt}       3398 {c |}{res}          1        0.77       96.92
{txt}       3423 {c |}{res}          1        0.77       97.69
{txt}       3458 {c |}{res}          1        0.77       98.46
{txt}       3471 {c |}{res}          1        0.77       99.23
{txt}       3484 {c |}{res}          1        0.77      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        130      100.00
{txt}
{com}. di 130/254
{res}.51181102
{txt}
{com}. 
. margins, dydx(treatment1) at(st_ethno=(0 (0.05) 1)) vce(unconditional)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}       254

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.treatment1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 8}.05}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 8}.15}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 8}.25}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 8}.35}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:9._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.4}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:10._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 8}.45}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:11._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.5}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:12._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 8}.55}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:13._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.6}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:14._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 8}.65}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:15._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.7}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:16._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 8}.75}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:17._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.8}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:18._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 8}.85}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:19._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.9}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:20._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 8}.95}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:21._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}  Linearized
{col 15}{c |}      dy/dx{col 27}   Std. Err.{col 39}      t{col 47}   P>|t|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treatment1  {txt}{c |}
{space 10}_at {c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.1518117{col 27}{space 2}  .070707{col 38}{space 1}   -2.15{col 47}{space 3}0.034{col 55}{space 4}-.2918558{col 68}{space 3}-.0117676
{txt}{space 11}2  {c |}{col 15}{res}{space 2} -.118061{col 27}{space 2} .0645581{col 38}{space 1}   -1.83{col 47}{space 3}0.070{col 55}{space 4}-.2459265{col 68}{space 3} .0098045
{txt}{space 11}3  {c |}{col 15}{res}{space 2}-.0843103{col 27}{space 2}  .058734{col 38}{space 1}   -1.44{col 47}{space 3}0.154{col 55}{space 4}-.2006403{col 68}{space 3} .0320197
{txt}{space 11}4  {c |}{col 15}{res}{space 2}-.0505596{col 27}{space 2}  .053341{col 38}{space 1}   -0.95{col 47}{space 3}0.345{col 55}{space 4}-.1562081{col 68}{space 3} .0550889
{txt}{space 11}5  {c |}{col 15}{res}{space 2}-.0168089{col 27}{space 2} .0485231{col 38}{space 1}   -0.35{col 47}{space 3}0.730{col 55}{space 4} -.112915{col 68}{space 3} .0792972
{txt}{space 11}6  {c |}{col 15}{res}{space 2} .0169418{col 27}{space 2} .0444677{col 38}{space 1}    0.38{col 47}{space 3}0.704{col 55}{space 4}-.0711321{col 68}{space 3} .1050157
{txt}{space 11}7  {c |}{col 15}{res}{space 2} .0506925{col 27}{space 2} .0413995{col 38}{space 1}    1.22{col 47}{space 3}0.223{col 55}{space 4}-.0313043{col 68}{space 3} .1326894
{txt}{space 11}8  {c |}{col 15}{res}{space 2} .0844432{col 27}{space 2} .0395488{col 38}{space 1}    2.14{col 47}{space 3}0.035{col 55}{space 4} .0061118{col 68}{space 3} .1627746
{txt}{space 11}9  {c |}{col 15}{res}{space 2} .1181939{col 27}{space 2}  .039089{col 38}{space 1}    3.02{col 47}{space 3}0.003{col 55}{space 4} .0407732{col 68}{space 3} .1956147
{txt}{space 10}10  {c |}{col 15}{res}{space 2} .1519446{col 27}{space 2}  .040068{col 38}{space 1}    3.79{col 47}{space 3}0.000{col 55}{space 4} .0725848{col 68}{space 3} .2313044
{txt}{space 10}11  {c |}{col 15}{res}{space 2} .1856954{col 27}{space 2} .0423863{col 38}{space 1}    4.38{col 47}{space 3}0.000{col 55}{space 4}  .101744{col 68}{space 3} .2696467
{txt}{space 10}12  {c |}{col 15}{res}{space 2} .2194461{col 27}{space 2}  .045841{col 38}{space 1}    4.79{col 47}{space 3}0.000{col 55}{space 4} .1286521{col 68}{space 3}   .31024
{txt}{space 10}13  {c |}{col 15}{res}{space 2} .2531968{col 27}{space 2} .0501982{col 38}{space 1}    5.04{col 47}{space 3}0.000{col 55}{space 4}  .153773{col 68}{space 3} .3526206
{txt}{space 10}14  {c |}{col 15}{res}{space 2} .2869475{col 27}{space 2} .0552446{col 38}{space 1}    5.19{col 47}{space 3}0.000{col 55}{space 4} .1775286{col 68}{space 3} .3963663
{txt}{space 10}15  {c |}{col 15}{res}{space 2} .3206982{col 27}{space 2} .0608089{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4} .2002585{col 68}{space 3} .4411379
{txt}{space 10}16  {c |}{col 15}{res}{space 2} .3544489{col 27}{space 2} .0667618{col 38}{space 1}    5.31{col 47}{space 3}0.000{col 55}{space 4} .2222188{col 68}{space 3}  .486679
{txt}{space 10}17  {c |}{col 15}{res}{space 2} .3881996{col 27}{space 2} .0730082{col 38}{space 1}    5.32{col 47}{space 3}0.000{col 55}{space 4} .2435976{col 68}{space 3} .5328016
{txt}{space 10}18  {c |}{col 15}{res}{space 2} .4219503{col 27}{space 2} .0794791{col 38}{space 1}    5.31{col 47}{space 3}0.000{col 55}{space 4}  .264532{col 68}{space 3} .5793686
{txt}{space 10}19  {c |}{col 15}{res}{space 2}  .455701{col 27}{space 2} .0861237{col 38}{space 1}    5.29{col 47}{space 3}0.000{col 55}{space 4} .2851222{col 68}{space 3} .6262798
{txt}{space 10}20  {c |}{col 15}{res}{space 2} .4894517{col 27}{space 2} .0929049{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4} .3054419{col 68}{space 3} .6734615
{txt}{space 10}21  {c |}{col 15}{res}{space 2} .5232024{col 27}{space 2} .0997947{col 38}{space 1}    5.24{col 47}{space 3}0.000{col 55}{space 4} .3255464{col 68}{space 3} .7208585
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 79}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. *Panel A
. marginsplot, xdimension(st_ethno) name(FigureL1_PanelA, replace) recast(line) recastci(rline) ciopts(lcolor(black)) ytitle("") ytitle(, size(medsmall)) yline(0, lpattern(solid)) ylabel(-0.4(0.2)0.8, angle(horizontal) grid glpattern(shortdash)) xtitle("", size(medsmall)) xlabel(0(0.1)1, labsize(medsmall)) title("""")

{text}{p 2 6 2}Variables that uniquely identify margins: st_ethno{p_end}
{res}{txt}
{com}. *Panel B
. histogram st_ethno if treatment1!=. & sex!=. & st_age!=. & proedu2!=. & work2, bin(10) percent fcolor(gs15) lcolor(gs2) ytitle("") ylabel(, labsize(medlarge) angle(horizontal) grid glpattern(solid)) xtitle("") xtitle(, size(medlarge)) xscale(range(0 1)) xlabel(0(0.1)1, labsize(medlarge)) name(FigureL1_PanelB, replace) xsize(5) ysize(3)
{txt}(bin={res}10{txt}, start={res}0{txt}, width={res}.1{txt})
{res}{txt}
{com}. 
. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      4.88
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1330

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0007221{col 48}{space 2} .0977606{col 59}{space 1}    0.01{col 68}{space 3}0.994{col 76}{space 4}-.1929051{col 89}{space 3} .1943494
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1518117{col 48}{space 2}  .070707{col 59}{space 1}   -2.15{col 68}{space 3}0.034{col 76}{space 4}-.2918558{col 89}{space 3}-.0117676
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6750141{col 48}{space 2} .1507665{col 59}{space 1}    4.48{col 68}{space 3}0.000{col 76}{space 4}  .376402{col 89}{space 3} .9736263
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0061306{col 48}{space 2} .0367219{col 59}{space 1}   -0.17{col 68}{space 3}0.868{col 76}{space 4}-.0788629{col 89}{space 3} .0666017
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7003869{col 48}{space 2} .3186851{col 59}{space 1}    2.20{col 68}{space 3}0.030{col 76}{space 4} .0691909{col 89}{space 3} 1.331583
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6554761{col 48}{space 2} .3602407{col 59}{space 1}   -1.82{col 68}{space 3}0.071{col 76}{space 4}-1.368978{col 89}{space 3} .0580259
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0975267{col 48}{space 2} .0645064{col 59}{space 1}   -1.51{col 68}{space 3}0.133{col 76}{space 4}-.2252897{col 89}{space 3} .0302363
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}    -.022{col 48}{space 2} .0792046{col 59}{space 1}   -0.28{col 68}{space 3}0.782{col 76}{space 4}-.1788748{col 89}{space 3} .1348747
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1095112{col 48}{space 2} .0748847{col 59}{space 1}   -1.46{col 68}{space 3}0.146{col 76}{space 4}-.2578299{col 89}{space 3} .0388074
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0841434{col 48}{space 2} .0728254{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2283832{col 89}{space 3} .0600964
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1028858{col 48}{space 2} .0890782{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2793165{col 89}{space 3} .0735448
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0070997{col 48}{space 2} .1194672{col 59}{space 1}   -0.06{col 68}{space 3}0.953{col 76}{space 4}-.2437196{col 89}{space 3} .2295203
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1023694{col 48}{space 2} .1167093{col 59}{space 1}   -0.88{col 68}{space 3}0.382{col 76}{space 4}-.3335268{col 89}{space 3}  .128788
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5497256{col 48}{space 2}    .1046{col 59}{space 1}    5.26{col 68}{space 3}0.000{col 76}{space 4} .3425521{col 89}{space 3} .7568991
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. sum st_ethno if e(sample), detail

                          {txt}st_ethno
{hline 61}
      Percentiles      Smallest
 1%    {res}      .05              0
{txt} 5%    {res} .1166667       .0333333
{txt}10%    {res} .1583333            .05       {txt}Obs         {res}        254
{txt}25%    {res}      .25       .0583333       {txt}Sum of Wgt. {res}        254

{txt}50%    {res} .3833333                      {txt}Mean          {res}  .397605
                        {txt}Largest       Std. Dev.     {res} .1995411
{txt}75%    {res} .5333334             .9
{txt}90%    {res}      .65             .9       {txt}Variance      {res} .0398166
{txt}95%    {res}     .775       .9166667       {txt}Skewness      {res} .5145561
{txt}99%    {res}       .9              1       {txt}Kurtosis      {res}  2.86251
{txt}
{com}. 
. margins, dydx(treatment1) at(st_ethno=(0.3833333)) vce(unconditional)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}       254

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.treatment1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.3833333}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}treatment1 {c |}
{space 2}Treatment  {c |}{col 14}{res}{space 2} .1069437{col 26}{space 2} .0390817{col 37}{space 1}    2.74{col 46}{space 3}0.007{col 54}{space 4} .0295375{col 67}{space 3} .1843498
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. margins, at (treatment1=(0,1) st_ethno=(0.3833333)) vce(unconditional)
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       254

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.3833333}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 3}.3833333}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5323958{col 26}{space 2} .0218959{col 37}{space 1}   24.31{col 46}{space 3}0.000{col 54}{space 4} .4890283{col 67}{space 3} .5757633
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6393395{col 26}{space 2} .0326736{col 37}{space 1}   19.57{col 46}{space 3}0.000{col 54}{space 4} .5746254{col 67}{space 3} .7040536
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. margins, dydx(treatment1) at(st_ethno=(0.9)) vce(unconditional)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}       254

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.treatment1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.9}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}treatment1 {c |}
{space 2}Treatment  {c |}{col 14}{res}{space 2}  .455701{col 26}{space 2} .0861237{col 37}{space 1}    5.29{col 46}{space 3}0.000{col 54}{space 4} .2851222{col 67}{space 3} .6262798
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. margins, at (treatment1=(0,1) st_ethno=(0.9)) vce(unconditional)
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       254

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.9}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 9}.9}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5327689{col 26}{space 2} .0556335{col 37}{space 1}    9.58{col 46}{space 3}0.000{col 54}{space 4} .4225798{col 67}{space 3}  .642958
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .9884699{col 26}{space 2} .0739931{col 37}{space 1}   13.36{col 46}{space 3}0.000{col 54}{space 4} .8419172{col 67}{space 3} 1.135023
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *-------------------------------------------------------------------------------
. 
{txt}end of do-file

{com}. do "Appendix_M.do"              
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. keep if german==1 & governmentsplit!=.                                  
{txt}(1,898 observations deleted)

{com}. 
. *-------------------------------------------------------------------------------
. *Appendix M: Robustness check: Modelling ethnocentrism categorically
. *-------------------------------------------------------------------------------
. 
.                                                                 ***Table M1***
. 
. 
. *Full ethnocentrism index and civil liberties with reference to terrorism
. *Upper 2/3 (66.81%) versus lower 1/3 of the observations (33.19%)
. svy: regress st_terrori c.ethno_upper2_3dummy i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  11{txt},{res}    106{txt}){col 67}= {res}      1.35
{txt}{col 49}Prob > F{col 67}= {res}    0.2086
{txt}{col 49}R-squared{col 67}= {res}    0.0586

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ethno_upper2_3dummy {c |}{col 36}{res}{space 2} .0355483{col 48}{space 2} .0401154{col 59}{space 1}    0.89{col 68}{space 3}0.377{col 76}{space 4}-.0439052{col 89}{space 3} .1150018
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0052214{col 48}{space 2} .0383267{col 59}{space 1}   -0.14{col 68}{space 3}0.892{col 76}{space 4}-.0811323{col 89}{space 3} .0706894
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .592951{col 48}{space 2} .3404026{col 59}{space 1}    1.74{col 68}{space 3}0.084{col 76}{space 4}-.0812592{col 89}{space 3} 1.267161
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5014028{col 48}{space 2} .3764539{col 59}{space 1}   -1.33{col 68}{space 3}0.186{col 76}{space 4}-1.247017{col 89}{space 3} .2442116
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1223221{col 48}{space 2} .0727042{col 59}{space 1}   -1.68{col 68}{space 3}0.095{col 76}{space 4}-.2663219{col 89}{space 3} .0216777
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0622449{col 48}{space 2} .0832743{col 59}{space 1}   -0.75{col 68}{space 3}0.456{col 76}{space 4}-.2271803{col 89}{space 3} .1026904
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1312044{col 48}{space 2} .0842538{col 59}{space 1}   -1.56{col 68}{space 3}0.122{col 76}{space 4}-.2980796{col 89}{space 3} .0356708
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0454682{col 48}{space 2} .0801362{col 59}{space 1}   -0.57{col 68}{space 3}0.572{col 76}{space 4}-.2041881{col 89}{space 3} .1132517
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0759364{col 48}{space 2} .0950006{col 59}{space 1}   -0.80{col 68}{space 3}0.426{col 76}{space 4} -.264097{col 89}{space 3} .1122242
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0029226{col 48}{space 2} .1223189{col 59}{space 1}    0.02{col 68}{space 3}0.981{col 76}{space 4}-.2393454{col 89}{space 3} .2451905
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0836234{col 48}{space 2} .1197661{col 59}{space 1}   -0.70{col 68}{space 3}0.486{col 76}{space 4}-.3208353{col 89}{space 3} .1535885
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5538194{col 48}{space 2} .1086093{col 59}{space 1}    5.10{col 68}{space 3}0.000{col 76}{space 4} .3387049{col 89}{space 3} .7689339
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.ethno_upper2_3dummy i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  12{txt},{res}    105{txt}){col 67}= {res}      1.58
{txt}{col 49}Prob > F{col 67}= {res}    0.1080
{txt}{col 49}R-squared{col 67}= {res}    0.0873

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ethno_upper2_3dummy {c |}{col 36}{res}{space 2} .0351514{col 48}{space 2} .0394112{col 59}{space 1}    0.89{col 68}{space 3}0.374{col 76}{space 4}-.0429073{col 89}{space 3} .1132102
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1163889{col 48}{space 2} .0409187{col 59}{space 1}    2.84{col 68}{space 3}0.005{col 76}{space 4} .0353443{col 89}{space 3} .1974334
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} -.013395{col 48}{space 2} .0383846{col 59}{space 1}   -0.35{col 68}{space 3}0.728{col 76}{space 4}-.0894206{col 89}{space 3} .0626306
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6149414{col 48}{space 2} .3325996{col 59}{space 1}    1.85{col 68}{space 3}0.067{col 76}{space 4} -.043814{col 89}{space 3} 1.273697
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5450182{col 48}{space 2} .3645194{col 59}{space 1}   -1.50{col 68}{space 3}0.138{col 76}{space 4}-1.266995{col 89}{space 3} .1769584
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1151945{col 48}{space 2} .0666193{col 59}{space 1}   -1.73{col 68}{space 3}0.086{col 76}{space 4}-.2471424{col 89}{space 3} .0167534
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0535529{col 48}{space 2} .0799751{col 59}{space 1}   -0.67{col 68}{space 3}0.504{col 76}{space 4}-.2119537{col 89}{space 3} .1048479
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1392421{col 48}{space 2} .0792074{col 59}{space 1}   -1.76{col 68}{space 3}0.081{col 76}{space 4}-.2961223{col 89}{space 3}  .017638
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0599593{col 48}{space 2} .0843556{col 59}{space 1}   -0.71{col 68}{space 3}0.479{col 76}{space 4}-.2270362{col 89}{space 3} .1071176
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0740938{col 48}{space 2} .0999559{col 59}{space 1}   -0.74{col 68}{space 3}0.460{col 76}{space 4} -.272069{col 89}{space 3} .1238814
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}  .019732{col 48}{space 2} .1262515{col 59}{space 1}    0.16{col 68}{space 3}0.876{col 76}{space 4}-.2303251{col 89}{space 3}  .269789
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0906516{col 48}{space 2} .1202214{col 59}{space 1}   -0.75{col 68}{space 3}0.452{col 76}{space 4}-.3287652{col 89}{space 3}  .147462
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5398427{col 48}{space 2} .1063956{col 59}{space 1}    5.07{col 68}{space 3}0.000{col 76}{space 4} .3291129{col 89}{space 3} .7505725
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.ethno_upper2_3dummy##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2  
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      2.09
{txt}{col 49}Prob > F{col 67}= {res}    0.0206
{txt}{col 49}R-squared{col 67}= {res}    0.1076

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ethno_upper2_3dummy {c |}{col 36}{res}{space 2}-.0016294{col 48}{space 2} .0426216{col 59}{space 1}   -0.04{col 68}{space 3}0.970{col 76}{space 4}-.0860468{col 89}{space 3}  .082788
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.0019601{col 48}{space 2} .0553044{col 59}{space 1}   -0.04{col 68}{space 3}0.972{col 76}{space 4}-.1114974{col 89}{space 3} .1075771
{txt}{space 34} {c |}
{space 2}treatment1#c.ethno_upper2_3dummy {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}  .197491{col 48}{space 2} .0761915{col 59}{space 1}    2.59{col 68}{space 3}0.011{col 76}{space 4} .0465841{col 89}{space 3} .3483978
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0098884{col 48}{space 2} .0382123{col 59}{space 1}   -0.26{col 68}{space 3}0.796{col 76}{space 4}-.0855727{col 89}{space 3} .0657959
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6831486{col 48}{space 2} .3201996{col 59}{space 1}    2.13{col 68}{space 3}0.035{col 76}{space 4}  .048953{col 89}{space 3} 1.317344
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6412277{col 48}{space 2} .3585894{col 59}{space 1}   -1.79{col 68}{space 3}0.076{col 76}{space 4}-1.351459{col 89}{space 3} .0690038
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1092526{col 48}{space 2} .0647719{col 59}{space 1}   -1.69{col 68}{space 3}0.094{col 76}{space 4}-.2375415{col 89}{space 3} .0190362
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0455913{col 48}{space 2} .0792769{col 59}{space 1}   -0.58{col 68}{space 3}0.566{col 76}{space 4}-.2026093{col 89}{space 3} .1114266
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1287082{col 48}{space 2} .0763183{col 59}{space 1}   -1.69{col 68}{space 3}0.094{col 76}{space 4}-.2798662{col 89}{space 3} .0224499
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0741387{col 48}{space 2} .0791878{col 59}{space 1}   -0.94{col 68}{space 3}0.351{col 76}{space 4}-.2309802{col 89}{space 3} .0827028
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0830044{col 48}{space 2} .0951196{col 59}{space 1}   -0.87{col 68}{space 3}0.385{col 76}{space 4}-.2714008{col 89}{space 3}  .105392
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0048753{col 48}{space 2} .1230739{col 59}{space 1}    0.04{col 68}{space 3}0.968{col 76}{space 4}-.2388881{col 89}{space 3} .2486387
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0990047{col 48}{space 2} .1195517{col 59}{space 1}   -0.83{col 68}{space 3}0.409{col 76}{space 4}-.3357918{col 89}{space 3} .1377825
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5591961{col 48}{space 2} .1038806{col 59}{space 1}    5.38{col 68}{space 3}0.000{col 76}{space 4} .3534475{col 89}{space 3} .7649447
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. *Upper 1/3 (33.52%) versus lower 2/3 of the observations (66.48%)
. tab ethno ethno_upper1_3dummy if treatment1!=.

           {txt}{c |}  ethno_upper1_3dummy
     ethno {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         1 {c |}{res}         1          0 {txt}{c |}{res}         1 
{txt}      1.15 {c |}{res}         1          0 {txt}{c |}{res}         1 
{txt}       1.2 {c |}{res}         2          0 {txt}{c |}{res}         2 
{txt}       1.3 {c |}{res}         1          0 {txt}{c |}{res}         1 
{txt}      1.35 {c |}{res}         3          0 {txt}{c |}{res}         3 
{txt}       1.4 {c |}{res}         1          0 {txt}{c |}{res}         1 
{txt}      1.45 {c |}{res}         1          0 {txt}{c |}{res}         1 
{txt}       1.5 {c |}{res}         1          0 {txt}{c |}{res}         1 
{txt}       1.6 {c |}{res}         1          0 {txt}{c |}{res}         1 
{txt}      1.65 {c |}{res}         2          0 {txt}{c |}{res}         2 
{txt}       1.7 {c |}{res}         2          0 {txt}{c |}{res}         2 
{txt}      1.75 {c |}{res}         7          0 {txt}{c |}{res}         7 
{txt}       1.8 {c |}{res}         1          0 {txt}{c |}{res}         1 
{txt}      1.85 {c |}{res}         1          0 {txt}{c |}{res}         1 
{txt}       1.9 {c |}{res}         2          0 {txt}{c |}{res}         2 
{txt}      1.95 {c |}{res}         5          0 {txt}{c |}{res}         5 
{txt}      2.05 {c |}{res}         3          0 {txt}{c |}{res}         3 
{txt}       2.1 {c |}{res}         2          0 {txt}{c |}{res}         2 
{txt}      2.15 {c |}{res}         6          0 {txt}{c |}{res}         6 
{txt}       2.2 {c |}{res}         3          0 {txt}{c |}{res}         3 
{txt}      2.25 {c |}{res}         4          0 {txt}{c |}{res}         4 
{txt}       2.3 {c |}{res}         5          0 {txt}{c |}{res}         5 
{txt}      2.35 {c |}{res}         7          0 {txt}{c |}{res}         7 
{txt}       2.4 {c |}{res}         2          0 {txt}{c |}{res}         2 
{txt}      2.45 {c |}{res}         5          0 {txt}{c |}{res}         5 
{txt}       2.5 {c |}{res}         8          0 {txt}{c |}{res}         8 
{txt}      2.55 {c |}{res}         8          0 {txt}{c |}{res}         8 
{txt}       2.6 {c |}{res}         4          0 {txt}{c |}{res}         4 
{txt}      2.65 {c |}{res}         6          0 {txt}{c |}{res}         6 
{txt}       2.7 {c |}{res}         3          0 {txt}{c |}{res}         3 
{txt}      2.75 {c |}{res}         3          0 {txt}{c |}{res}         3 
{txt}       2.8 {c |}{res}         2          0 {txt}{c |}{res}         2 
{txt}      2.85 {c |}{res}         4          0 {txt}{c |}{res}         4 
{txt}       2.9 {c |}{res}         3          0 {txt}{c |}{res}         3 
{txt}      2.95 {c |}{res}         4          0 {txt}{c |}{res}         4 
{txt}         3 {c |}{res}         1          0 {txt}{c |}{res}         1 
{txt}      3.05 {c |}{res}         4          0 {txt}{c |}{res}         4 
{txt}       3.1 {c |}{res}         5          0 {txt}{c |}{res}         5 
{txt}      3.15 {c |}{res}         4          0 {txt}{c |}{res}         4 
{txt}       3.2 {c |}{res}         8          0 {txt}{c |}{res}         8 
{txt}      3.25 {c |}{res}         2          0 {txt}{c |}{res}         2 
{txt}       3.3 {c |}{res}         8          0 {txt}{c |}{res}         8 
{txt}      3.35 {c |}{res}         3          0 {txt}{c |}{res}         3 
{txt}       3.4 {c |}{res}         4          0 {txt}{c |}{res}         4 
{txt}      3.45 {c |}{res}         4          0 {txt}{c |}{res}         4 
{txt}       3.5 {c |}{res}         8          0 {txt}{c |}{res}         8 
{txt}      3.55 {c |}{res}         3          0 {txt}{c |}{res}         3 
{txt}       3.6 {c |}{res}         5          0 {txt}{c |}{res}         5 
{txt}      3.65 {c |}{res}         1          0 {txt}{c |}{res}         1 
{txt}       3.7 {c |}{res}         3          0 {txt}{c |}{res}         3 
{txt}      3.75 {c |}{res}         6          0 {txt}{c |}{res}         6 
{txt}       3.8 {c |}{res}         3          0 {txt}{c |}{res}         3 
{txt}      3.85 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}       3.9 {c |}{res}         0          4 {txt}{c |}{res}         4 
{txt}      3.95 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}         4 {c |}{res}         0          4 {txt}{c |}{res}         4 
{txt}      4.05 {c |}{res}         0          4 {txt}{c |}{res}         4 
{txt}       4.1 {c |}{res}         0          5 {txt}{c |}{res}         5 
{txt}      4.15 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}       4.2 {c |}{res}         0          3 {txt}{c |}{res}         3 
{txt}      4.25 {c |}{res}         0          3 {txt}{c |}{res}         3 
{txt}       4.3 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}      4.35 {c |}{res}         0          3 {txt}{c |}{res}         3 
{txt}       4.4 {c |}{res}         0          3 {txt}{c |}{res}         3 
{txt}      4.45 {c |}{res}         0          3 {txt}{c |}{res}         3 
{txt}       4.5 {c |}{res}         0          6 {txt}{c |}{res}         6 
{txt}      4.55 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}       4.6 {c |}{res}         0          3 {txt}{c |}{res}         3 
{txt}      4.65 {c |}{res}         0          3 {txt}{c |}{res}         3 
{txt}       4.7 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}      4.75 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}      4.85 {c |}{res}         0          4 {txt}{c |}{res}         4 
{txt}       4.9 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}      4.95 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}         5 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}      5.05 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}       5.1 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}       5.2 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}       5.3 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}       5.4 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}       5.5 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}      5.55 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}       5.6 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}      5.65 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}      5.75 {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}       5.8 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}         6 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}       6.3 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}       6.4 {c |}{res}         0          4 {txt}{c |}{res}         4 
{txt}       6.5 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}      6.65 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}         7 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       186         90 {txt}{c |}{res}       276 

{txt}
{com}. svy: regress st_terrori c.ethno_upper1_3dummy i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  11{txt},{res}    106{txt}){col 67}= {res}      1.89
{txt}{col 49}Prob > F{col 67}= {res}    0.0479
{txt}{col 49}R-squared{col 67}= {res}    0.0685

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ethno_upper1_3dummy {c |}{col 36}{res}{space 2} .0692805{col 48}{space 2} .0384401{col 59}{space 1}    1.80{col 68}{space 3}0.074{col 76}{space 4} -.006855{col 89}{space 3} .1454159
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0049378{col 48}{space 2} .0380351{col 59}{space 1}   -0.13{col 68}{space 3}0.897{col 76}{space 4}-.0802712{col 89}{space 3} .0703956
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6141866{col 48}{space 2} .3477736{col 59}{space 1}    1.77{col 68}{space 3}0.080{col 76}{space 4}-.0746229{col 89}{space 3} 1.302996
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5308805{col 48}{space 2} .3823491{col 59}{space 1}   -1.39{col 68}{space 3}0.168{col 76}{space 4}-1.288171{col 89}{space 3} .2264101
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1365491{col 48}{space 2}  .071279{col 59}{space 1}   -1.92{col 68}{space 3}0.058{col 76}{space 4}-.2777261{col 89}{space 3} .0046279
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0703776{col 48}{space 2} .0833653{col 59}{space 1}   -0.84{col 68}{space 3}0.400{col 76}{space 4}-.2354931{col 89}{space 3} .0947379
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1435282{col 48}{space 2} .0802888{col 59}{space 1}   -1.79{col 68}{space 3}0.076{col 76}{space 4}-.3025503{col 89}{space 3}  .015494
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0372769{col 48}{space 2} .0833907{col 59}{space 1}   -0.45{col 68}{space 3}0.656{col 76}{space 4}-.2024428{col 89}{space 3} .1278889
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0653976{col 48}{space 2} .0974362{col 59}{space 1}   -0.67{col 68}{space 3}0.503{col 76}{space 4}-.2583822{col 89}{space 3}  .127587
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0241201{col 48}{space 2} .1237222{col 59}{space 1}    0.19{col 68}{space 3}0.846{col 76}{space 4}-.2209273{col 89}{space 3} .2691675
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0606112{col 48}{space 2} .1269628{col 59}{space 1}   -0.48{col 68}{space 3}0.634{col 76}{space 4}-.3120771{col 89}{space 3} .1908547
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5536908{col 48}{space 2} .1076975{col 59}{space 1}    5.14{col 68}{space 3}0.000{col 76}{space 4} .3403823{col 89}{space 3} .7669993
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.ethno_upper1_3dummy i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  12{txt},{res}    105{txt}){col 67}= {res}      2.26
{txt}{col 49}Prob > F{col 67}= {res}    0.0137
{txt}{col 49}R-squared{col 67}= {res}    0.0940

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ethno_upper1_3dummy {c |}{col 36}{res}{space 2} .0604904{col 48}{space 2} .0373144{col 59}{space 1}    1.62{col 68}{space 3}0.108{col 76}{space 4}-.0134154{col 89}{space 3} .1343962
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1100677{col 48}{space 2}  .041107{col 59}{space 1}    2.68{col 68}{space 3}0.008{col 76}{space 4}   .02865{col 89}{space 3} .1914853
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0128866{col 48}{space 2} .0381452{col 59}{space 1}   -0.34{col 68}{space 3}0.736{col 76}{space 4}-.0884379{col 89}{space 3} .0626648
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6334897{col 48}{space 2} .3389852{col 59}{space 1}    1.87{col 68}{space 3}0.064{col 76}{space 4}-.0379133{col 89}{space 3} 1.304893
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5667082{col 48}{space 2} .3687971{col 59}{space 1}   -1.54{col 68}{space 3}0.127{col 76}{space 4}-1.297157{col 89}{space 3} .1637408
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1293126{col 48}{space 2} .0661881{col 59}{space 1}   -1.95{col 68}{space 3}0.053{col 76}{space 4}-.2604064{col 89}{space 3} .0017813
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0624827{col 48}{space 2} .0808476{col 59}{space 1}   -0.77{col 68}{space 3}0.441{col 76}{space 4}-.2226116{col 89}{space 3} .0976461
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1514858{col 48}{space 2} .0772702{col 59}{space 1}   -1.96{col 68}{space 3}0.052{col 76}{space 4}-.3045291{col 89}{space 3} .0015574
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0525699{col 48}{space 2} .0869895{col 59}{space 1}   -0.60{col 68}{space 3}0.547{col 76}{space 4}-.2248636{col 89}{space 3} .1197237
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0657241{col 48}{space 2} .1018148{col 59}{space 1}   -0.65{col 68}{space 3}0.520{col 76}{space 4}-.2673811{col 89}{space 3} .1359329
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0371079{col 48}{space 2} .1274499{col 59}{space 1}    0.29{col 68}{space 3}0.771{col 76}{space 4}-.2153228{col 89}{space 3} .2895385
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0701844{col 48}{space 2} .1266653{col 59}{space 1}   -0.55{col 68}{space 3}0.581{col 76}{space 4} -.321061{col 89}{space 3} .1806922
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .544051{col 48}{space 2} .1053154{col 59}{space 1}    5.17{col 68}{space 3}0.000{col 76}{space 4} .3354606{col 89}{space 3} .7526413
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.ethno_upper1_3dummy##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      3.50
{txt}{col 49}Prob > F{col 67}= {res}    0.0002
{txt}{col 49}R-squared{col 67}= {res}    0.1257

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ethno_upper1_3dummy {c |}{col 36}{res}{space 2} .0054782{col 48}{space 2}  .039322{col 59}{space 1}    0.14{col 68}{space 3}0.889{col 76}{space 4}-.0724039{col 89}{space 3} .0833603
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .0183631{col 48}{space 2} .0495971{col 59}{space 1}    0.37{col 68}{space 3}0.712{col 76}{space 4}-.0798702{col 89}{space 3} .1165963
{txt}{space 34} {c |}
{space 2}treatment1#c.ethno_upper1_3dummy {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}  .252643{col 48}{space 2} .0702056{col 59}{space 1}    3.60{col 68}{space 3}0.000{col 76}{space 4} .1135919{col 89}{space 3}  .391694
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0182745{col 48}{space 2} .0373403{col 59}{space 1}   -0.49{col 68}{space 3}0.625{col 76}{space 4}-.0922316{col 89}{space 3} .0556827
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7029849{col 48}{space 2} .3288392{col 59}{space 1}    2.14{col 68}{space 3}0.035{col 76}{space 4} .0516775{col 89}{space 3} 1.354292
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6305546{col 48}{space 2} .3710928{col 59}{space 1}   -1.70{col 68}{space 3}0.092{col 76}{space 4}-1.365551{col 89}{space 3} .1044415
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} -.113791{col 48}{space 2} .0663683{col 59}{space 1}   -1.71{col 68}{space 3}0.089{col 76}{space 4}-.2452418{col 89}{space 3} .0176598
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0443661{col 48}{space 2} .0807916{col 59}{space 1}   -0.55{col 68}{space 3}0.584{col 76}{space 4} -.204384{col 89}{space 3} .1156519
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1317454{col 48}{space 2} .0751862{col 59}{space 1}   -1.75{col 68}{space 3}0.082{col 76}{space 4}-.2806611{col 89}{space 3} .0171703
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0708225{col 48}{space 2} .0818541{col 59}{space 1}   -0.87{col 68}{space 3}0.389{col 76}{space 4}-.2329449{col 89}{space 3}    .0913
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0885989{col 48}{space 2} .0981354{col 59}{space 1}   -0.90{col 68}{space 3}0.368{col 76}{space 4}-.2829684{col 89}{space 3} .1057706
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0146797{col 48}{space 2} .1249499{col 59}{space 1}    0.12{col 68}{space 3}0.907{col 76}{space 4}-.2327994{col 89}{space 3} .2621588
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0736073{col 48}{space 2} .1219466{col 59}{space 1}   -0.60{col 68}{space 3}0.547{col 76}{space 4} -.315138{col 89}{space 3} .1679233
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5496935{col 48}{space 2} .0998593{col 59}{space 1}    5.50{col 68}{space 3}0.000{col 76}{space 4} .3519096{col 89}{space 3} .7474774
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. 
. 
. 
. 
. 
. 
. 
. 
{txt}end of do-file

{com}. do "Appendix_N.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. keep if german==1 & governmentsplit!=.                                  
{txt}(1,898 observations deleted)

{com}. 
. *-------------------------------------------------------------------------------
. *Appendix N: Robustness check: The activation effect using an alternative ethnocentrism measure
. *-------------------------------------------------------------------------------
. 
.                                                                 ***Table N1***
. 
. *Alternative ethnocentrism measure: "It is better for a country if all people belong to a common culture"
. svy: regress st_terrori c.st_homocult i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       269
{txt}{col 1}Number of PSUs{col 20}= {res}      122{txt}{col 49}Population size{col 67}={res} 265.354109
{txt}{col 49}Design df{col 67}= {res}       121
{txt}{col 49}F({res}  11{txt},{res}    111{txt}){col 67}= {res}      1.28
{txt}{col 49}Prob > F{col 67}= {res}    0.2427
{txt}{col 49}R-squared{col 67}= {res}    0.0491

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}st_homocult {c |}{col 36}{res}{space 2}-.0358576{col 48}{space 2} .0678125{col 59}{space 1}   -0.53{col 68}{space 3}0.598{col 76}{space 4}-.1701103{col 89}{space 3} .0983952
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}   .00759{col 48}{space 2} .0365973{col 59}{space 1}    0.21{col 68}{space 3}0.836{col 76}{space 4} -.064864{col 89}{space 3} .0800439
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4046066{col 48}{space 2} .3143402{col 59}{space 1}    1.29{col 68}{space 3}0.200{col 76}{space 4}-.2177127{col 89}{space 3} 1.026926
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2650068{col 48}{space 2}   .36732{col 59}{space 1}   -0.72{col 68}{space 3}0.472{col 76}{space 4}-.9922137{col 89}{space 3} .4622001
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1483315{col 48}{space 2} .0708401{col 59}{space 1}   -2.09{col 68}{space 3}0.038{col 76}{space 4}-.2885782{col 89}{space 3}-.0080848
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0971785{col 48}{space 2} .0831439{col 59}{space 1}   -1.17{col 68}{space 3}0.245{col 76}{space 4}-.2617838{col 89}{space 3} .0674267
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1647085{col 48}{space 2} .0793811{col 59}{space 1}   -2.07{col 68}{space 3}0.040{col 76}{space 4}-.3218643{col 89}{space 3}-.0075527
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0041917{col 48}{space 2}  .078426{col 59}{space 1}   -0.05{col 68}{space 3}0.957{col 76}{space 4}-.1594565{col 89}{space 3} .1510732
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0397615{col 48}{space 2} .0935288{col 59}{space 1}   -0.43{col 68}{space 3}0.671{col 76}{space 4}-.2249264{col 89}{space 3} .1454033
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0384927{col 48}{space 2} .1188641{col 59}{space 1}    0.32{col 68}{space 3}0.747{col 76}{space 4}-.1968301{col 89}{space 3} .2738155
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0611087{col 48}{space 2} .1144367{col 59}{space 1}   -0.53{col 68}{space 3}0.594{col 76}{space 4}-.2876664{col 89}{space 3}  .165449
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5929054{col 48}{space 2} .1033829{col 59}{space 1}    5.74{col 68}{space 3}0.000{col 76}{space 4} .3882317{col 89}{space 3} .7975791
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_homocult i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       269
{txt}{col 1}Number of PSUs{col 20}= {res}      122{txt}{col 49}Population size{col 67}={res} 265.354109
{txt}{col 49}Design df{col 67}= {res}       121
{txt}{col 49}F({res}  12{txt},{res}    110{txt}){col 67}= {res}      1.45
{txt}{col 49}Prob > F{col 67}= {res}    0.1530
{txt}{col 49}R-squared{col 67}= {res}    0.0706

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}st_homocult {c |}{col 36}{res}{space 2}-.0326738{col 48}{space 2} .0654809{col 59}{space 1}   -0.50{col 68}{space 3}0.619{col 76}{space 4}-.1623104{col 89}{space 3} .0969628
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1002655{col 48}{space 2} .0408923{col 59}{space 1}    2.45{col 68}{space 3}0.016{col 76}{space 4} .0193084{col 89}{space 3} .1812226
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}  .000374{col 48}{space 2}  .037012{col 59}{space 1}    0.01{col 68}{space 3}0.992{col 76}{space 4} -.072901{col 89}{space 3} .0736491
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .427678{col 48}{space 2} .3068933{col 59}{space 1}    1.39{col 68}{space 3}0.166{col 76}{space 4}-.1798982{col 89}{space 3} 1.035254
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.3070856{col 48}{space 2} .3576617{col 59}{space 1}   -0.86{col 68}{space 3}0.392{col 76}{space 4}-1.015171{col 89}{space 3} .4010002
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1435907{col 48}{space 2} .0658358{col 59}{space 1}   -2.18{col 68}{space 3}0.031{col 76}{space 4}  -.27393{col 89}{space 3}-.0132513
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0918405{col 48}{space 2} .0813559{col 59}{space 1}   -1.13{col 68}{space 3}0.261{col 76}{space 4}-.2529059{col 89}{space 3}  .069225
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1730443{col 48}{space 2} .0764804{col 59}{space 1}   -2.26{col 68}{space 3}0.025{col 76}{space 4}-.3244574{col 89}{space 3}-.0216312
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0114642{col 48}{space 2} .0794402{col 59}{space 1}   -0.14{col 68}{space 3}0.885{col 76}{space 4}-.1687371{col 89}{space 3} .1458087
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0333227{col 48}{space 2} .0954279{col 59}{space 1}   -0.35{col 68}{space 3}0.728{col 76}{space 4}-.2222474{col 89}{space 3}  .155602
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0584742{col 48}{space 2} .1218018{col 59}{space 1}    0.48{col 68}{space 3}0.632{col 76}{space 4}-.1826646{col 89}{space 3}  .299613
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0610966{col 48}{space 2} .1136811{col 59}{space 1}   -0.54{col 68}{space 3}0.592{col 76}{space 4}-.2861584{col 89}{space 3} .1639652
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5751886{col 48}{space 2} .1018002{col 59}{space 1}    5.65{col 68}{space 3}0.000{col 76}{space 4} .3736482{col 89}{space 3}  .776729
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_homocult##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2  
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       269
{txt}{col 1}Number of PSUs{col 20}= {res}      122{txt}{col 49}Population size{col 67}={res} 265.354109
{txt}{col 49}Design df{col 67}= {res}       121
{txt}{col 49}F({res}  13{txt},{res}    109{txt}){col 67}= {res}      3.26
{txt}{col 49}Prob > F{col 67}= {res}    0.0003
{txt}{col 49}R-squared{col 67}= {res}    0.1078

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}st_homocult {c |}{col 36}{res}{space 2}-.1131492{col 48}{space 2}  .069422{col 59}{space 1}   -1.63{col 68}{space 3}0.106{col 76}{space 4}-.2505883{col 89}{space 3}   .02429
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.0495469{col 48}{space 2} .0568848{col 59}{space 1}   -0.87{col 68}{space 3}0.385{col 76}{space 4}-.1621655{col 89}{space 3} .0630716
{txt}{space 34} {c |}
{space 10}treatment1#c.st_homocult {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .4395983{col 48}{space 2} .1079293{col 59}{space 1}    4.07{col 68}{space 3}0.000{col 76}{space 4} .2259237{col 89}{space 3} .6532728
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0069231{col 48}{space 2} .0362421{col 59}{space 1}    0.19{col 68}{space 3}0.849{col 76}{space 4}-.0648276{col 89}{space 3} .0786739
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4954594{col 48}{space 2} .3013505{col 59}{space 1}    1.64{col 68}{space 3}0.103{col 76}{space 4}-.1011435{col 89}{space 3} 1.092062
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.4028037{col 48}{space 2} .3617514{col 59}{space 1}   -1.11{col 68}{space 3}0.268{col 76}{space 4}-1.118986{col 89}{space 3} .3133787
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1104847{col 48}{space 2} .0646502{col 59}{space 1}   -1.71{col 68}{space 3}0.090{col 76}{space 4}-.2384767{col 89}{space 3} .0175073
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0541876{col 48}{space 2} .0800075{col 59}{space 1}   -0.68{col 68}{space 3}0.500{col 76}{space 4}-.2125835{col 89}{space 3} .1042084
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1366962{col 48}{space 2} .0730123{col 59}{space 1}   -1.87{col 68}{space 3}0.064{col 76}{space 4}-.2812434{col 89}{space 3}  .007851
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0225668{col 48}{space 2} .0748292{col 59}{space 1}   -0.30{col 68}{space 3}0.763{col 76}{space 4} -.170711{col 89}{space 3} .1255773
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0468104{col 48}{space 2} .0925047{col 59}{space 1}   -0.51{col 68}{space 3}0.614{col 76}{space 4}-.2299478{col 89}{space 3}  .136327
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0276991{col 48}{space 2} .1180614{col 59}{space 1}    0.23{col 68}{space 3}0.815{col 76}{space 4}-.2060347{col 89}{space 3} .2614328
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0684077{col 48}{space 2} .1127885{col 59}{space 1}   -0.61{col 68}{space 3}0.545{col 76}{space 4}-.2917024{col 89}{space 3} .1548869
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5740983{col 48}{space 2} .0994952{col 59}{space 1}    5.77{col 68}{space 3}0.000{col 76}{space 4} .3771213{col 89}{space 3} .7710752
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. *-------------------------------------------------------------------------------
. 
{txt}end of do-file

{com}. do "Appendix_O.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. keep if german==1 & governmentsplit!=.                                  
{txt}(1,898 observations deleted)

{com}. 
. *-------------------------------------------------------------------------------
. *Appendix O: Robustness check: Assessing the activation of political ideology 
. *-------------------------------------------------------------------------------
. 
.                                                                 ***Table O1***
. 
. tab lrID                        

 {txt}Left-right {c |}
self-placem {c |}
        ent {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Left {c |}{res}         42        2.75        2.75
{txt}          2 {c |}{res}         56        3.66        6.41
{txt}          3 {c |}{res}        190       12.42       18.82
{txt}          4 {c |}{res}        190       12.42       31.24
{txt}          5 {c |}{res}        359       23.46       54.71
{txt}          6 {c |}{res}        431       28.17       82.88
{txt}          7 {c |}{res}        166       10.85       93.73
{txt}          8 {c |}{res}         66        4.31       98.04
{txt}          9 {c |}{res}         13        0.85       98.89
{txt}      Right {c |}{res}         17        1.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,530      100.00
{txt}
{com}. *Full ethnocentrism index and civil liberties with reference to terrorism
. svy: regress st_terrori c.st_lrID i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       262
{txt}{col 1}Number of PSUs{col 20}= {res}      121{txt}{col 49}Population size{col 67}={res} 258.118275
{txt}{col 49}Design df{col 67}= {res}       120
{txt}{col 49}F({res}  11{txt},{res}    110{txt}){col 67}= {res}      1.50
{txt}{col 49}Prob > F{col 67}= {res}    0.1401
{txt}{col 49}R-squared{col 67}= {res}    0.0586

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}st_lrID {c |}{col 36}{res}{space 2} .1314623{col 48}{space 2}   .09465{col 59}{space 1}    1.39{col 68}{space 3}0.167{col 76}{space 4} -.055938{col 89}{space 3} .3188627
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0236264{col 48}{space 2} .0369511{col 59}{space 1}    0.64{col 68}{space 3}0.524{col 76}{space 4}-.0495341{col 89}{space 3}  .096787
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .3384524{col 48}{space 2} .3268323{col 59}{space 1}    1.04{col 68}{space 3}0.302{col 76}{space 4}-.3086528{col 89}{space 3} .9855576
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} -.197165{col 48}{space 2} .3808239{col 59}{space 1}   -0.52{col 68}{space 3}0.606{col 76}{space 4}-.9511699{col 89}{space 3} .5568399
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1700898{col 48}{space 2} .0699518{col 59}{space 1}   -2.43{col 68}{space 3}0.017{col 76}{space 4}-.3085896{col 89}{space 3}-.0315901
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1238769{col 48}{space 2} .0773629{col 59}{space 1}   -1.60{col 68}{space 3}0.112{col 76}{space 4}  -.27705{col 89}{space 3} .0292962
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1841449{col 48}{space 2} .0760759{col 59}{space 1}   -2.42{col 68}{space 3}0.017{col 76}{space 4}  -.33477{col 89}{space 3}-.0335199
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0127466{col 48}{space 2} .0778815{col 59}{space 1}    0.16{col 68}{space 3}0.870{col 76}{space 4}-.1414533{col 89}{space 3} .1669465
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0341747{col 48}{space 2} .0907787{col 59}{space 1}   -0.38{col 68}{space 3}0.707{col 76}{space 4}-.2139102{col 89}{space 3} .1455609
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0011957{col 48}{space 2}  .114406{col 59}{space 1}   -0.01{col 68}{space 3}0.992{col 76}{space 4}-.2277116{col 89}{space 3} .2253203
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0335539{col 48}{space 2} .1093921{col 59}{space 1}   -0.31{col 68}{space 3}0.760{col 76}{space 4}-.2501427{col 89}{space 3} .1830349
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5315176{col 48}{space 2} .1143125{col 59}{space 1}    4.65{col 68}{space 3}0.000{col 76}{space 4} .3051867{col 89}{space 3} .7578485
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_lrID i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       262
{txt}{col 1}Number of PSUs{col 20}= {res}      121{txt}{col 49}Population size{col 67}={res} 258.118275
{txt}{col 49}Design df{col 67}= {res}       120
{txt}{col 49}F({res}  12{txt},{res}    109{txt}){col 67}= {res}      1.69
{txt}{col 49}Prob > F{col 67}= {res}    0.0786
{txt}{col 49}R-squared{col 67}= {res}    0.0814

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}st_lrID {c |}{col 36}{res}{space 2} .1375957{col 48}{space 2} .0938918{col 59}{space 1}    1.47{col 68}{space 3}0.145{col 76}{space 4}-.0483036{col 89}{space 3}  .323495
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1011032{col 48}{space 2} .0390789{col 59}{space 1}    2.59{col 68}{space 3}0.011{col 76}{space 4} .0237298{col 89}{space 3} .1784766
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0163085{col 48}{space 2} .0371305{col 59}{space 1}    0.44{col 68}{space 3}0.661{col 76}{space 4}-.0572072{col 89}{space 3} .0898242
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .3644104{col 48}{space 2} .3164144{col 59}{space 1}    1.15{col 68}{space 3}0.252{col 76}{space 4} -.262068{col 89}{space 3} .9908888
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2395027{col 48}{space 2} .3674739{col 59}{space 1}   -0.65{col 68}{space 3}0.516{col 76}{space 4}-.9670754{col 89}{space 3} .4880701
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1635805{col 48}{space 2} .0641528{col 59}{space 1}   -2.55{col 68}{space 3}0.012{col 76}{space 4}-.2905986{col 89}{space 3}-.0365623
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1165426{col 48}{space 2} .0752003{col 59}{space 1}   -1.55{col 68}{space 3}0.124{col 76}{space 4}-.2654339{col 89}{space 3} .0323487
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1907647{col 48}{space 2} .0730318{col 59}{space 1}   -2.61{col 68}{space 3}0.010{col 76}{space 4}-.3353626{col 89}{space 3}-.0461667
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0045981{col 48}{space 2} .0789529{col 59}{space 1}    0.06{col 68}{space 3}0.954{col 76}{space 4} -.151723{col 89}{space 3} .1609193
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0287081{col 48}{space 2} .0923239{col 59}{space 1}   -0.31{col 68}{space 3}0.756{col 76}{space 4} -.211503{col 89}{space 3} .1540867
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0183923{col 48}{space 2} .1167867{col 59}{space 1}    0.16{col 68}{space 3}0.875{col 76}{space 4}-.2128372{col 89}{space 3} .2496219
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0365479{col 48}{space 2}  .108921{col 59}{space 1}   -0.34{col 68}{space 3}0.738{col 76}{space 4} -.252204{col 89}{space 3} .1791082
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5095346{col 48}{space 2} .1096373{col 59}{space 1}    4.65{col 68}{space 3}0.000{col 76}{space 4} .2924603{col 89}{space 3} .7266089
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_lrID##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       262
{txt}{col 1}Number of PSUs{col 20}= {res}      121{txt}{col 49}Population size{col 67}={res} 258.118275
{txt}{col 49}Design df{col 67}= {res}       120
{txt}{col 49}F({res}  13{txt},{res}    108{txt}){col 67}= {res}      2.28
{txt}{col 49}Prob > F{col 67}= {res}    0.0107
{txt}{col 49}R-squared{col 67}= {res}    0.0895

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 27}st_lrID {c |}{col 36}{res}{space 2} .0596521{col 48}{space 2} .1058593{col 59}{space 1}    0.56{col 68}{space 3}0.574{col 76}{space 4} -.149942{col 89}{space 3} .2692461
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.0464575{col 48}{space 2} .0940614{col 59}{space 1}   -0.49{col 68}{space 3}0.622{col 76}{space 4}-.2326924{col 89}{space 3} .1397774
{txt}{space 34} {c |}
{space 14}treatment1#c.st_lrID {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .3203638{col 48}{space 2} .1740809{col 59}{space 1}    1.84{col 68}{space 3}0.068{col 76}{space 4}-.0243042{col 89}{space 3} .6650318
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0184625{col 48}{space 2} .0364362{col 59}{space 1}    0.51{col 68}{space 3}0.613{col 76}{space 4}-.0536787{col 89}{space 3} .0906037
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4169701{col 48}{space 2} .3146064{col 59}{space 1}    1.33{col 68}{space 3}0.188{col 76}{space 4}-.2059287{col 89}{space 3} 1.039869
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2973544{col 48}{space 2} .3687221{col 59}{space 1}   -0.81{col 68}{space 3}0.422{col 76}{space 4}-1.027399{col 89}{space 3} .4326897
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} -.164159{col 48}{space 2}  .064036{col 59}{space 1}   -2.56{col 68}{space 3}0.012{col 76}{space 4}-.2909459{col 89}{space 3}-.0373721
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} -.118993{col 48}{space 2} .0748438{col 59}{space 1}   -1.59{col 68}{space 3}0.114{col 76}{space 4}-.2671786{col 89}{space 3} .0291925
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} -.191126{col 48}{space 2} .0727494{col 59}{space 1}   -2.63{col 68}{space 3}0.010{col 76}{space 4}-.3351646{col 89}{space 3}-.0470873
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0022806{col 48}{space 2} .0747521{col 59}{space 1}    0.03{col 68}{space 3}0.976{col 76}{space 4}-.1457234{col 89}{space 3} .1502847
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0300999{col 48}{space 2} .0897346{col 59}{space 1}   -0.34{col 68}{space 3}0.738{col 76}{space 4}-.2077682{col 89}{space 3} .1475684
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0128104{col 48}{space 2} .1148599{col 59}{space 1}    0.11{col 68}{space 3}0.911{col 76}{space 4}-.2146042{col 89}{space 3}  .240225
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0374363{col 48}{space 2} .1067345{col 59}{space 1}   -0.35{col 68}{space 3}0.726{col 76}{space 4}-.2487632{col 89}{space 3} .1738906
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .539184{col 48}{space 2} .1103941{col 59}{space 1}    4.88{col 68}{space 3}0.000{col 76}{space 4} .3206115{col 89}{space 3} .7577566
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. *Interaction model including both ethnocentrism and left-right self-placement
. svy: regress st_terrori c.st_ethno##i.treatment1 c.st_lrID##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       248
{txt}{col 1}Number of PSUs{col 20}= {res}      116{txt}{col 49}Population size{col 67}={res} 244.347084
{txt}{col 49}Design df{col 67}= {res}       115
{txt}{col 49}F({res}  15{txt},{res}    101{txt}){col 67}= {res}      4.44
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1366

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2}-.0508156{col 48}{space 2} .1211473{col 59}{space 1}   -0.42{col 68}{space 3}0.676{col 76}{space 4} -.290785{col 89}{space 3} .1891539
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1526509{col 48}{space 2} .0985347{col 59}{space 1}   -1.55{col 68}{space 3}0.124{col 76}{space 4}-.3478291{col 89}{space 3} .0425273
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6476663{col 48}{space 2} .1858209{col 59}{space 1}    3.49{col 68}{space 3}0.001{col 76}{space 4} .2795909{col 89}{space 3} 1.015742
{txt}{space 34} {c |}
{space 27}st_lrID {c |}{col 36}{res}{space 2} .0907574{col 48}{space 2} .1263033{col 59}{space 1}    0.72{col 68}{space 3}0.474{col 76}{space 4} -.159425{col 89}{space 3} .3409399
{txt}{space 34} {c |}
{space 14}treatment1#c.st_lrID {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .0239053{col 48}{space 2} .2276516{col 59}{space 1}    0.11{col 68}{space 3}0.917{col 76}{space 4}-.4270287{col 89}{space 3} .4748393
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0048095{col 48}{space 2}  .036788{col 59}{space 1}    0.13{col 68}{space 3}0.896{col 76}{space 4}-.0680605{col 89}{space 3} .0776796
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5962075{col 48}{space 2} .3184527{col 59}{space 1}    1.87{col 68}{space 3}0.064{col 76}{space 4} -.034586{col 89}{space 3} 1.227001
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5296898{col 48}{space 2} .3751296{col 59}{space 1}   -1.41{col 68}{space 3}0.161{col 76}{space 4}-1.272749{col 89}{space 3} .2133697
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1288832{col 48}{space 2} .0642802{col 59}{space 1}   -2.01{col 68}{space 3}0.047{col 76}{space 4}-.2562098{col 89}{space 3}-.0015565
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0640268{col 48}{space 2} .0751756{col 59}{space 1}   -0.85{col 68}{space 3}0.396{col 76}{space 4}-.2129352{col 89}{space 3} .0848816
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1433032{col 48}{space 2} .0744726{col 59}{space 1}   -1.92{col 68}{space 3}0.057{col 76}{space 4}-.2908191{col 89}{space 3} .0042127
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0798051{col 48}{space 2} .0714922{col 59}{space 1}   -1.12{col 68}{space 3}0.267{col 76}{space 4}-.2214174{col 89}{space 3} .0618072
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1065587{col 48}{space 2}   .08582{col 59}{space 1}   -1.24{col 68}{space 3}0.217{col 76}{space 4}-.2765516{col 89}{space 3} .0634341
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0604382{col 48}{space 2} .1157241{col 59}{space 1}   -0.52{col 68}{space 3}0.602{col 76}{space 4}-.2896654{col 89}{space 3} .1687889
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} -.100705{col 48}{space 2} .1129063{col 59}{space 1}   -0.89{col 68}{space 3}0.374{col 76}{space 4}-.3243507{col 89}{space 3} .1229407
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5684328{col 48}{space 2} .1070547{col 59}{space 1}    5.31{col 68}{space 3}0.000{col 76}{space 4}  .356378{col 89}{space 3} .7804875
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label(proper) replace
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt""':seeout}

{com}.  
.                         
.                         
. 
{txt}end of do-file

{com}. do "Appendix_P.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. keep if german==1 & governmentsplit!=.                                  
{txt}(1,898 observations deleted)

{com}. 
. *-------------------------------------------------------------------------------
. *Appendix P: Additional robustness checks: The activation effect over time
. *-------------------------------------------------------------------------------
. 
. 
.                                                                         
. /*Rolling window regressions
> To asses the effect of the activation effect over time, we generate a set of 
> pre-attacks and post-attacks groups, where the "window"  - meaning the 7 consequetive 
> days after the attacks - for the post-attack treatment is moved by 1 day at the
> time. 
> 
> As the date variable (edate) is not continuous we generated the window groups 
> manually. The first window variable, window1, is equivalent to the first
> post-attack group in the treatment1 variable. This means that it consists
> of those respondents who were interviewed in the first 7 days after the Würzburg
> attack. The second window group consists of those who were interviewed between 
> the second and eighth day after the attack. This procedure is continued until 
> 42 windows have been generated.
> 
> Each moving window is compared to the pre-attacks group. Thus, the category 0 in 
> each dummy variable equals the pre-attacks group (those interviewed the 20 days 
> before the Nice attack) and 1 equals the respective moving window. 
> 
> */
. 
. ********************************************************************************
. 
. 
. *1: Generating windows for dummy variables
. gen window1 = 1 if respid!=. & (edate>=td(15/07/2016) & edate<=td(21/07/2016))
{txt}(1,532 missing values generated)

{com}. gen window2 = 1 if respid!=. & (edate>=td(16/07/2016) & edate<=td(22/07/2016))
{txt}(1,527 missing values generated)

{com}. gen window3 = 1 if respid!=. & (edate>=td(17/07/2016) & edate<=td(23/07/2016))
{txt}(1,530 missing values generated)

{com}. gen window4 = 1 if respid!=. & (edate>=td(18/07/2016) & edate<=td(24/07/2016))
{txt}(1,530 missing values generated)

{com}. gen window5 = 1 if respid!=. & (edate>=td(19/07/2016) & edate<=td(25/07/2016))
{txt}(1,528 missing values generated)

{com}. gen window6 = 1 if respid!=. & (edate>=td(20/07/2016) & edate<=td(26/07/2016))
{txt}(1,534 missing values generated)

{com}. gen window7 = 1 if respid!=. & (edate>=td(21/07/2016) & edate<=td(27/07/2016))
{txt}(1,533 missing values generated)

{com}. gen window8 = 1 if respid!=. & (edate>=td(22/07/2016) & edate<=td(28/07/2016))
{txt}(1,533 missing values generated)

{com}. gen window9 = 1 if respid!=. & (edate>=td(23/07/2016) & edate<=td(29/07/2016))
{txt}(1,534 missing values generated)

{com}. gen window10 = 1 if respid!=. & (edate>=td(24/07/2016) & edate<=td(30/07/2016))
{txt}(1,535 missing values generated)

{com}. gen window11 = 1 if respid!=. & (edate>=td(25/07/2016) & edate<=td(31/07/2016))
{txt}(1,535 missing values generated)

{com}. gen window12 = 1 if respid!=. & (edate>=td(26/07/2016) & edate<=td(01/08/2016))
{txt}(1,533 missing values generated)

{com}. gen window13 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(02/08/2016))
{txt}(1,533 missing values generated)

{com}. gen window14 = 1 if respid!=. & (edate>=td(28/07/2016) & edate<=td(03/08/2016))
{txt}(1,540 missing values generated)

{com}. gen window15 = 1 if respid!=. & (edate>=td(29/07/2016) & edate<=td(04/08/2016))
{txt}(1,544 missing values generated)

{com}. gen window16 = 1 if respid!=. & (edate>=td(30/07/2016) & edate<=td(05/08/2016))
{txt}(1,548 missing values generated)

{com}. gen window17 = 1 if respid!=. & (edate>=td(31/07/2016) & edate<=td(06/08/2016))
{txt}(1,547 missing values generated)

{com}. gen window18 = 1 if respid!=. & (edate>=td(01/08/2016) & edate<=td(07/08/2016))
{txt}(1,547 missing values generated)

{com}. gen window19 = 1 if respid!=. & (edate>=td(02/08/2016) & edate<=td(08/08/2016))
{txt}(1,553 missing values generated)

{com}. gen window20 = 1 if respid!=. & (edate>=td(03/08/2016) & edate<=td(09/08/2016))
{txt}(1,555 missing values generated)

{com}. gen window21 = 1 if respid!=. & (edate>=td(04/08/2016) & edate<=td(10/08/2016))
{txt}(1,556 missing values generated)

{com}. gen window22 = 1 if respid!=. & (edate>=td(05/08/2016) & edate<=td(11/08/2016))
{txt}(1,555 missing values generated)

{com}. gen window23 = 1 if respid!=. & (edate>=td(06/08/2016) & edate<=td(12/08/2016))
{txt}(1,552 missing values generated)

{com}. gen window24 = 1 if respid!=. & (edate>=td(07/08/2016) & edate<=td(13/08/2016))
{txt}(1,552 missing values generated)

{com}. gen window25 = 1 if respid!=. & (edate>=td(08/08/2016) & edate<=td(14/08/2016))
{txt}(1,551 missing values generated)

{com}. gen window26 = 1 if respid!=. & (edate>=td(09/08/2016) & edate<=td(15/08/2016))
{txt}(1,556 missing values generated)

{com}. gen window27 = 1 if respid!=. & (edate>=td(10/08/2016) & edate<=td(16/08/2016))
{txt}(1,560 missing values generated)

{com}. gen window28 = 1 if respid!=. & (edate>=td(11/08/2016) & edate<=td(17/08/2016))
{txt}(1,559 missing values generated)

{com}. gen window29 = 1 if respid!=. & (edate>=td(12/08/2016) & edate<=td(18/08/2016))
{txt}(1,559 missing values generated)

{com}. gen window30 = 1 if respid!=. & (edate>=td(13/08/2016) & edate<=td(19/08/2016))
{txt}(1,566 missing values generated)

{com}. gen window31 = 1 if respid!=. & (edate>=td(14/08/2016) & edate<=td(20/08/2016))
{txt}(1,568 missing values generated)

{com}. gen window32 = 1 if respid!=. & (edate>=td(15/08/2016) & edate<=td(21/08/2016))
{txt}(1,569 missing values generated)

{com}. gen window33 = 1 if respid!=. & (edate>=td(16/08/2016) & edate<=td(22/08/2016))
{txt}(1,569 missing values generated)

{com}. gen window34 = 1 if respid!=. & (edate>=td(17/08/2016) & edate<=td(23/08/2016))
{txt}(1,570 missing values generated)

{com}. gen window35 = 1 if respid!=. & (edate>=td(18/08/2016) & edate<=td(24/08/2016))
{txt}(1,570 missing values generated)

{com}. gen window36 = 1 if respid!=. & (edate>=td(19/08/2016) & edate<=td(25/08/2016))
{txt}(1,574 missing values generated)

{com}. gen window37 = 1 if respid!=. & (edate>=td(20/08/2016) & edate<=td(26/08/2016))
{txt}(1,569 missing values generated)

{com}. gen window38 = 1 if respid!=. & (edate>=td(21/08/2016) & edate<=td(27/08/2016))
{txt}(1,568 missing values generated)

{com}. gen window39 = 1 if respid!=. & (edate>=td(22/08/2016) & edate<=td(28/08/2016))
{txt}(1,568 missing values generated)

{com}. gen window40 = 1 if respid!=. & (edate>=td(23/08/2016) & edate<=td(29/08/2016))
{txt}(1,568 missing values generated)

{com}. gen window41 = 1 if respid!=. & (edate>=td(24/08/2016) & edate<=td(30/08/2016))
{txt}(1,567 missing values generated)

{com}. gen window42 = 1 if respid!=. & (edate>=td(25/08/2016) & edate<=td(31/08/2016))
{txt}(1,566 missing values generated)

{com}. 
. 
. 
. 
. foreach var of varlist window* {c -(}
{txt}  2{com}.         gen c`var' = `var'
{txt}  3{com}. {c )-}
{txt}(1,532 missing values generated)
(1,527 missing values generated)
(1,530 missing values generated)
(1,530 missing values generated)
(1,528 missing values generated)
(1,534 missing values generated)
(1,533 missing values generated)
(1,533 missing values generated)
(1,534 missing values generated)
(1,535 missing values generated)
(1,535 missing values generated)
(1,533 missing values generated)
(1,533 missing values generated)
(1,540 missing values generated)
(1,544 missing values generated)
(1,548 missing values generated)
(1,547 missing values generated)
(1,547 missing values generated)
(1,553 missing values generated)
(1,555 missing values generated)
(1,556 missing values generated)
(1,555 missing values generated)
(1,552 missing values generated)
(1,552 missing values generated)
(1,551 missing values generated)
(1,556 missing values generated)
(1,560 missing values generated)
(1,559 missing values generated)
(1,559 missing values generated)
(1,566 missing values generated)
(1,568 missing values generated)
(1,569 missing values generated)
(1,569 missing values generated)
(1,570 missing values generated)
(1,570 missing values generated)
(1,574 missing values generated)
(1,569 missing values generated)
(1,568 missing values generated)
(1,568 missing values generated)
(1,568 missing values generated)
(1,567 missing values generated)
(1,566 missing values generated)

{com}. 
. *Replacing missing values in dummy variables with 0 for observations in the pre-
. *attacks group.
. 
. foreach var of varlist cwindow* {c -(}
{txt}  2{com}.         replace `var' = 0 if treatment1==0      
{txt}  3{com}. {c )-}
{txt}(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)

{com}. 
. save GGSS2016_matched_windows.dta, replace
{txt}file GGSS2016_matched_windows.dta saved

{com}. 
. 
. ********************************************************************************
. 
. *2: Estimating main model for each window-dummy and storing results
. 
. /*Here, we estimate the main model (table 1, model 3) for each window variable 
> using the main dependent variable - civil liberties with reference to terrorism
>  - and storing the coefficient for the interaction term, the standard error, 
> and the p-value in separate data files. The same process is repeated for 
> authoritarianism and left-right political ideology, although these are
> not displayed in the Online Appendix.*/
. 
. *Ethnocentrism
. tempname e_t_C
{txt}
{com}. postfile `e_t_C'  e_t_c_coef e_t_c_SE e_t_c_pval using e_t_C.dta, replace
{txt}
{com}. foreach var of varlist cwindow*{c -(}
{txt}  2{com}.         qui svy: regress st_terrori c.`var'##c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2
{txt}  3{com}.         matrix define temp5=r(table)
{txt}  4{com}.         post `e_t_C'  (`=_b[`var'#st_ethno]') (`=_se[`var'#st_ethno]') (`=temp5[4,3]')
{txt}  5{com}. {c )-}
{txt}
{com}. postclose `e_t_C'
{txt}
{com}. 
. 
. *Authorchild (not shown in Appendix)
. tempname ac_t_C
{txt}
{com}. postfile `ac_t_C'  ac_t_c_coef ac_t_c_SE ac_t_c_pval using ac_t_C.dta, replace
{txt}
{com}. foreach var of varlist cwindow*{c -(}
{txt}  2{com}.         qui svy: regress st_terrori c.`var'##c.st_authorchild i.sex st_age st_age2 i.proedu2 i.work2
{txt}  3{com}.         matrix define temp5=r(table)
{txt}  4{com}.         post `ac_t_C'  (`=_b[`var'#st_authorchild]') (`=_se[`var'#st_authorchild]') (`=temp5[4,3]')
{txt}  5{com}. {c )-}
{txt}
{com}. postclose `ac_t_C'
{txt}
{com}. 
. 
. *Left-right (not shown in Appendix)
. tempname lr_t_C
{txt}
{com}. postfile `lr_t_C'  lr_t_c_coef lr_t_c_SE lr_t_c_pval using lr_t_C.dta, replace
{txt}
{com}. foreach var of varlist cwindow*{c -(}
{txt}  2{com}.         qui svy: regress st_terrori c.`var'##c.st_lrID i.sex st_age st_age2 i.proedu2 i.work2
{txt}  3{com}.         matrix define temp5=r(table)
{txt}  4{com}.         post `lr_t_C'  (`=_b[`var'#st_lrID]') (`=_se[`var'#st_lrID]') (`=temp5[4,3]')
{txt}  5{com}. {c )-}
{txt}
{com}. postclose `lr_t_C'
{txt}
{com}. 
. 
. 
. 
. 
. ********************************************************************************
. 
. *3: Merging datasets into one dataset
. 
. *Merging ethnocentrism and authorchild datasets
. clear
{txt}
{com}. use e_t_C.dta 
{txt}
{com}. gen startday =_n
{txt}
{com}. save e_t_C.dta, replace
{txt}file e_t_C.dta saved

{com}. 
. clear
{txt}
{com}. use ac_t_C.dta
{txt}
{com}. gen startday =_n
{txt}
{com}. merge 1:1 startday using e_t_C.dta
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}               0
{txt}{col 5}matched{col 30}{res}              42{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. save e_ac_t_C.dta, replace
{txt}file e_ac_t_C.dta saved

{com}. 
. *Merging with authorleaders
. clear 
{txt}
{com}. use lr_t_C.dta
{txt}
{com}. gen startday =_n
{txt}
{com}. merge 1:1 startday using e_ac_t_C.dta
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}               0
{txt}{col 5}matched{col 30}{res}              42{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. 
. ********************************************************************************
. 
. *4: Generating variables for 95% CI's, upper and lower limits
. *Ethnocentrism
. gen e_c_LB = e_t_c_coef - 1.96*e_t_c_SE
{txt}
{com}. gen e_c_UB = e_t_c_coef + 1.96*e_t_c_SE
{txt}
{com}. 
. 
. *Authorchild
. gen ac_c_LB = ac_t_c_coef - 1.96*ac_t_c_SE
{txt}
{com}. gen ac_c_UB = ac_t_c_coef + 1.96*ac_t_c_SE
{txt}
{com}. 
. 
. *Left-right self-placement
. gen lr_c_LB = lr_t_c_coef - 1.96*lr_t_c_SE
{txt}
{com}. gen lr_c_UB = lr_t_c_coef + 1.96*lr_t_c_SE
{txt}
{com}. 
. order startday ///
>         e_t_c_coef e_t_c_SE e_c_LB e_c_UB e_t_c_pval ///
>         ac_t_c_coef ac_t_c_SE ac_c_LB ac_c_UB ac_t_c_pval ///
>         lr_t_c_coef lr_t_c_SE lr_c_LB lr_c_UB lr_t_c_pval 
{txt}
{com}. 
. save Time_figure.dta, replace
{txt}file Time_figure.dta saved

{com}. 
. 
. 
. 
. ********************************************************************************
. 
. *5: Plotting the estimatet coefficients and calculated 95%-CIs
. 
. 
.                                                                 ***Figure P1***
. 
. 
. *The activation of ethnocentrism in the first three weeks after the Normandy 
. *terrorist attack
. 
. clear 
{txt}
{com}. use Time_figure.dta
{txt}
{com}. 
. gen twodays=!1 if inlist(startday, 2, 4, 6 , 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42)
{txt}(21 missing values generated)

{com}.         
. twoway (connected e_t_c_coef startday if startday<=32, mcolor(gs3) msymbol(square_hollow) lcolor(gs3)) ///
>         (scatter e_t_c_coef startday if startday<=32 & e_t_c_pval<=0.05, mcolor(gs3) msymbol(square)), ///
>         ytitle(Treatment effect (Interaction Term)) yline(0, lcolor(black)) ylabel(-0.4(0.2)0.8, labsize(medsmall) ///
>         angle(horizontal) grid) xtitle(Days after Nice Terrorist Attack) xtitle(, size(medsmall)) ///
>         xscale(range(0 30)) xscale(nofextend) xline(0 4 8 10 12, lcolor(black)) ///
>         xlabel(0(2)32, labels grid) xmtick(none, labels) ///
>         legend(order(1 "Ethnocentrism (insignificant)" 2 "Ethnocentrism (significant)") size(medsmall) region(lcolor(none))) xsize(7)
{res}{txt}
{com}.         
. *Write city/town names for terrorist attacks manually in the graph editor.
. 
. 
. ********************************************************************************
. 
.                                         ***NOT SHOWN OR DESCRIBED IN ONLINE APPENDIX***
. 
. *Authoritariansim
. 
. *The activation of authoritariansism in the first three weeks after the Normandy 
. *terrorist attack
. 
. twoway (connected ac_t_c_coef startday if startday<=32, mcolor(gs3) msymbol(square_hollow) lcolor(gs3)) ///
>         (scatter ac_t_c_coef startday if startday<=32 & ac_t_c_pval<=0.05, mcolor(gs3) msymbol(square)), ///
>         ytitle(Treatment effect (Interaction Term)) yline(0, lcolor(black)) ylabel(-0.4(0.2)0.8, labsize(medsmall) ///
>         angle(horizontal) grid) xtitle(Days after Nice Terrorist Attack) xtitle(, size(medsmall)) ///
>         xscale(range(0 30)) xscale(nofextend) xline(0 4 8 10 12, lcolor(black)) ///
>         xlabel(0(2)32, labels grid) xmtick(none, labels) ///
>         legend(order(1 "Authoritarianism (insignificant)" 2 "Authoritariansim (significant)") size(medsmall) region(lcolor(none))) xsize(7)
{res}{txt}
{com}. 
. 
.         
. *Left-right self-placement
. 
. *The activation of Left-right self-placement in the first three weeks after the 
. *Normandy terrorist attack
. 
. twoway (connected lr_t_c_coef startday if startday<=32, mcolor(gs3) msymbol(square_hollow) lcolor(gs3)) ///
>         (scatter lr_t_c_coef startday if startday<=32 & lr_t_c_pval<=0.05, mcolor(gs3) msymbol(square)), ///
>         ytitle(Treatment effect (Interaction Term)) yline(0, lcolor(black)) ylabel(-0.4(0.2)0.8, labsize(medsmall) ///
>         angle(horizontal) grid) xtitle(Days after Nice Terrorist Attack) xtitle(, size(medsmall)) ///
>         xscale(range(0 30)) xscale(nofextend) xline(0 4 8 10 12, lcolor(black)) ///
>         xlabel(0(2)32, labels grid) xmtick(none, labels) ///
>         legend(order(1 "Left-right self-placement (insignificant)" 2 "Left-right self-placement (significant)") size(medsmall) region(lcolor(none))) xsize(7)
{res}{txt}
{com}. 
. 
{txt}end of do-file

{com}. do "Appendix_Q.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. keep if german==1 & governmentsplit!=.                                  
{txt}(1,898 observations deleted)

{com}. 
. *-------------------------------------------------------------------------------
. *Appendix Q: Additional robustness checks: the interaction affect for wider 
. *post-attacks groups  
. *-------------------------------------------------------------------------------
. 
. 
. *-------------------------------------------------------------------------------
.         ***The interaction effect when widening the post-attacks group by 1 day***
. *-------------------------------------------------------------------------------
. 
. 
. /*In the following, the main model is recalculated for new definitions of the 
> post-attacks group, which is widened by one day until the post-attacks group 
> consist of the respondents interviewed in the first 30 days after the attacks.
> The pre-attacks group remains the same, meaning the respondents interviewed in 
> the 20 days prior to the wave of terrorist attacks in July 2016.
> */
. 
. ********************************************************************************
. 
. *1: Generating window dummy variables
. gen treatmentwidth7 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(02/08/2016))
{txt}(1,533 missing values generated)

{com}. gen treatmentwidth8 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(03/08/2016))
{txt}(1,529 missing values generated)

{com}. gen treatmentwidth9 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(04/08/2016))
{txt}(1,523 missing values generated)

{com}. gen treatmentwidth10 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(05/08/2016))
{txt}(1,517 missing values generated)

{com}. gen treatmentwidth11 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(06/08/2016))
{txt}(1,515 missing values generated)

{com}. gen treatmentwidth12 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(07/08/2016))
{txt}(1,515 missing values generated)

{com}. gen treatmentwidth13 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(08/08/2016))
{txt}(1,505 missing values generated)

{com}. gen treatmentwidth14 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(09/08/2016))
{txt}(1,496 missing values generated)

{com}. gen treatmentwidth15 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(10/08/2016))
{txt}(1,493 missing values generated)

{com}. gen treatmentwidth16 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(11/08/2016))
{txt}(1,486 missing values generated)

{com}. gen treatmentwidth17 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(12/08/2016))
{txt}(1,477 missing values generated)

{com}. gen treatmentwidth18 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(13/08/2016))
{txt}(1,475 missing values generated)

{com}. gen treatmentwidth19 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(14/08/2016))
{txt}(1,474 missing values generated)

{com}. gen treatmentwidth20 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(15/08/2016))
{txt}(1,469 missing values generated)

{com}. gen treatmentwidth21 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(16/08/2016))
{txt}(1,464 missing values generated)

{com}. gen treatmentwidth22 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(17/08/2016))
{txt}(1,460 missing values generated)

{com}. gen treatmentwidth23 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(18/08/2016))
{txt}(1,453 missing values generated)

{com}. gen treatmentwidth24 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(19/08/2016))
{txt}(1,451 missing values generated)

{com}. gen treatmentwidth25 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(20/08/2016))
{txt}(1,451 missing values generated)

{com}. gen treatmentwidth26 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(21/08/2016))
{txt}(1,451 missing values generated)

{com}. gen treatmentwidth27 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(22/08/2016))
{txt}(1,446 missing values generated)

{com}. gen treatmentwidth28 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(23/08/2016))
{txt}(1,442 missing values generated)

{com}. gen treatmentwidth29 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(24/08/2016))
{txt}(1,438 missing values generated)

{com}. gen treatmentwidth30 = 1 if respid!=. & (edate>=td(27/07/2016) & edate<=td(25/08/2016))
{txt}(1,435 missing values generated)

{com}. 
. 
. 
. 
. foreach var of varlist treatmentwidth* {c -(}
{txt}  2{com}.         gen c`var' = `var'
{txt}  3{com}. {c )-}
{txt}(1,533 missing values generated)
(1,529 missing values generated)
(1,523 missing values generated)
(1,517 missing values generated)
(1,515 missing values generated)
(1,515 missing values generated)
(1,505 missing values generated)
(1,496 missing values generated)
(1,493 missing values generated)
(1,486 missing values generated)
(1,477 missing values generated)
(1,475 missing values generated)
(1,474 missing values generated)
(1,469 missing values generated)
(1,464 missing values generated)
(1,460 missing values generated)
(1,453 missing values generated)
(1,451 missing values generated)
(1,451 missing values generated)
(1,451 missing values generated)
(1,446 missing values generated)
(1,442 missing values generated)
(1,438 missing values generated)
(1,435 missing values generated)

{com}. 
. *Dummy variables
. *Control=0 
. foreach var of varlist ctreatmentwidth* {c -(}
{txt}  2{com}.         replace `var' = 0 if treatment1==0      
{txt}  3{com}. {c )-}
{txt}(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)
(238 real changes made)

{com}. 
. save GGSS2016_treatmentwide.dta, replace
{txt}file GGSS2016_treatmentwide.dta saved

{com}. ********************************************************************************
. 
. 
. *2: Estimating interaction effect for each window and variable per matched sample
. 
. *Interaction effects on civil liberties with reference to terrorism
. *Ethnocentrism
. tempname e_twidth_C
{txt}
{com}. postfile `e_twidth_C'  e_twidth_c_coef e_twidth_c_SE e_twidth_c_pval using e_twidth_C.dta, replace
{txt}
{com}. foreach var of varlist ctreatmentwidth*{c -(}
{txt}  2{com}.         qui svy: regress st_terrori c.`var'##c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2
{txt}  3{com}.         matrix define temp5=r(table)
{txt}  4{com}.         post `e_twidth_C'  (`=_b[`var'#st_ethno]') (`=_se[`var'#st_ethno]') (`=temp5[4,3]')
{txt}  5{com}. {c )-}
{txt}
{com}. postclose `e_twidth_C'
{txt}
{com}. ********************************************************************************
. 
. 
. *3: Generating variables for 95% CI's, upper and lower limits
. clear
{txt}
{com}. use e_twidth_C.dta 
{txt}
{com}. gen startday =_n
{txt}
{com}. gen treatdays = startday + 6
{txt}
{com}. save e_twidth_C.dta, replace
{txt}file e_twidth_C.dta saved

{com}. gen e_cwidth_LB = e_twidth_c_coef - 1.96*e_twidth_c_SE
{txt}
{com}. gen e_cwidth_UB = e_twidth_c_coef + 1.96*e_twidth_c_SE
{txt}
{com}. 
. 
. 
. order startday ///
>         e_twidth_c_coef e_twidth_c_SE e_cwidth_LB e_cwidth_UB e_twidth_c_pval 
{txt}
{com}. save treatmentwidth_figure.dta, replace
{txt}file treatmentwidth_figure.dta saved

{com}. ********************************************************************************
. 
. 
. *4: Plotting the estimated coefficients and calculated 95%-CIs
. 
.                                                         ***Figure Q1***
. 
. clear 
{txt}
{com}. use treatmentwidth_figure.dta
{txt}
{com}. 
. twoway (connected e_twidth_c_coef treatdays, mcolor(gs3) msymbol(circle_hollow) ///
> lcolor(gs3)) (scatter e_twidth_c_coef treatdays if e_twidth_c_pval<=0.05, ///
> mcolor(black) msymbol(circle)), ytitle(Activation Effect (Interaction Term)) ///
> ytitle(, size(medium)) ylabel(, labsize(medium) angle(horizontal) grid) ///
> ymtick(0.3(0.05)0.7, grid) xtitle(Expanding width of post-attacks group by 1 day) ///
> xtitle(, size(medium) margin(medium)) xlabel(7(1)30, labsize(medium) grid) ///
> legend(order(1 "Ethnocentrism (insignificant)" 2 "Ethnocentrism (significant)") ///
> rows(1) size(medium) region(lcolor(none))) xsize(7)     
{res}{txt}
{com}. ********************************************************************************
. 
. 
. *5: Checking the regressions
. clear 
{txt}
{com}. use GGSS2016_treatmentwide.dta
{txt}(ALLBUS 2016)

{com}. 
. 
. foreach var of varlist ctreatmentwidth*{c -(}
{txt}  2{com}.         svy: regress st_terrori c.`var'##c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2
{txt}  3{com}. {c )-}
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      4.88
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1330

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}ctreatmentwidth7 {c |}{col 36}{res}{space 2}-.1518117{col 48}{space 2}  .070707{col 59}{space 1}   -2.15{col 68}{space 3}0.034{col 76}{space 4}-.2918558{col 89}{space 3}-.0117676
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0007221{col 48}{space 2} .0977606{col 59}{space 1}    0.01{col 68}{space 3}0.994{col 76}{space 4}-.1929051{col 89}{space 3} .1943494
{txt}{space 34} {c |}
{space 5}c.ctreatmentwidth7#c.st_ethno {c |}{col 36}{res}{space 2} .6750141{col 48}{space 2} .1507665{col 59}{space 1}    4.48{col 68}{space 3}0.000{col 76}{space 4}  .376402{col 89}{space 3} .9736263
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0061306{col 48}{space 2} .0367219{col 59}{space 1}   -0.17{col 68}{space 3}0.868{col 76}{space 4}-.0788629{col 89}{space 3} .0666017
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7003869{col 48}{space 2} .3186851{col 59}{space 1}    2.20{col 68}{space 3}0.030{col 76}{space 4} .0691909{col 89}{space 3} 1.331583
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6554761{col 48}{space 2} .3602407{col 59}{space 1}   -1.82{col 68}{space 3}0.071{col 76}{space 4}-1.368978{col 89}{space 3} .0580259
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0975267{col 48}{space 2} .0645064{col 59}{space 1}   -1.51{col 68}{space 3}0.133{col 76}{space 4}-.2252897{col 89}{space 3} .0302363
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}    -.022{col 48}{space 2} .0792046{col 59}{space 1}   -0.28{col 68}{space 3}0.782{col 76}{space 4}-.1788748{col 89}{space 3} .1348747
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1095112{col 48}{space 2} .0748847{col 59}{space 1}   -1.46{col 68}{space 3}0.146{col 76}{space 4}-.2578299{col 89}{space 3} .0388074
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0841434{col 48}{space 2} .0728254{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2283832{col 89}{space 3} .0600964
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1028858{col 48}{space 2} .0890782{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2793165{col 89}{space 3} .0735448
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0070997{col 48}{space 2} .1194672{col 59}{space 1}   -0.06{col 68}{space 3}0.953{col 76}{space 4}-.2437196{col 89}{space 3} .2295203
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1023694{col 48}{space 2} .1167093{col 59}{space 1}   -0.88{col 68}{space 3}0.382{col 76}{space 4}-.3335268{col 89}{space 3}  .128788
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5497256{col 48}{space 2}    .1046{col 59}{space 1}    5.26{col 68}{space 3}0.000{col 76}{space 4} .3425521{col 89}{space 3} .7568991
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       258
{txt}{col 1}Number of PSUs{col 20}= {res}      120{txt}{col 49}Population size{col 67}={res} 255.984875
{txt}{col 49}Design df{col 67}= {res}       119
{txt}{col 49}F({res}  13{txt},{res}    107{txt}){col 67}= {res}      4.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1200

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}ctreatmentwidth8 {c |}{col 36}{res}{space 2}-.1528294{col 48}{space 2} .0696158{col 59}{space 1}   -2.20{col 68}{space 3}0.030{col 76}{space 4}-.2906755{col 89}{space 3}-.0149832
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0019342{col 48}{space 2} .0979653{col 59}{space 1}    0.02{col 68}{space 3}0.984{col 76}{space 4}-.1920468{col 89}{space 3} .1959153
{txt}{space 34} {c |}
{space 5}c.ctreatmentwidth8#c.st_ethno {c |}{col 36}{res}{space 2} .6226041{col 48}{space 2} .1491134{col 59}{space 1}    4.18{col 68}{space 3}0.000{col 76}{space 4} .3273448{col 89}{space 3} .9178635
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0036761{col 48}{space 2} .0366792{col 59}{space 1}   -0.10{col 68}{space 3}0.920{col 76}{space 4}-.0763046{col 89}{space 3} .0689524
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .634275{col 48}{space 2} .3207279{col 59}{space 1}    1.98{col 68}{space 3}0.050{col 76}{space 4}-.0007983{col 89}{space 3} 1.269348
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5203322{col 48}{space 2} .3619549{col 59}{space 1}   -1.44{col 68}{space 3}0.153{col 76}{space 4}-1.237039{col 89}{space 3} .1963745
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1088532{col 48}{space 2} .0649937{col 59}{space 1}   -1.67{col 68}{space 3}0.097{col 76}{space 4}-.2375472{col 89}{space 3} .0198409
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0321682{col 48}{space 2} .0782299{col 59}{space 1}   -0.41{col 68}{space 3}0.682{col 76}{space 4}-.1870712{col 89}{space 3} .1227348
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1136429{col 48}{space 2} .0756465{col 59}{space 1}   -1.50{col 68}{space 3}0.136{col 76}{space 4}-.2634306{col 89}{space 3} .0361447
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0772357{col 48}{space 2}   .07412{col 59}{space 1}   -1.04{col 68}{space 3}0.300{col 76}{space 4}-.2240008{col 89}{space 3} .0695293
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1321631{col 48}{space 2} .0877313{col 59}{space 1}   -1.51{col 68}{space 3}0.135{col 76}{space 4}-.3058799{col 89}{space 3} .0415536
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0079654{col 48}{space 2} .1193348{col 59}{space 1}   -0.07{col 68}{space 3}0.947{col 76}{space 4}-.2442601{col 89}{space 3} .2283294
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0974966{col 48}{space 2} .1177528{col 59}{space 1}   -0.83{col 68}{space 3}0.409{col 76}{space 4}-.3306588{col 89}{space 3} .1356657
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5586873{col 48}{space 2} .1053654{col 59}{space 1}    5.30{col 68}{space 3}0.000{col 76}{space 4} .3500533{col 89}{space 3} .7673213
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       264
{txt}{col 1}Number of PSUs{col 20}= {res}      124{txt}{col 49}Population size{col 67}={res} 261.986882
{txt}{col 49}Design df{col 67}= {res}       123
{txt}{col 49}F({res}  13{txt},{res}    111{txt}){col 67}= {res}      4.31
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1266

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}ctreatmentwidth9 {c |}{col 36}{res}{space 2}-.1653079{col 48}{space 2} .0669387{col 59}{space 1}   -2.47{col 68}{space 3}0.015{col 76}{space 4}-.2978089{col 89}{space 3}-.0328069
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0026995{col 48}{space 2} .0974265{col 59}{space 1}    0.03{col 68}{space 3}0.978{col 76}{space 4}-.1901502{col 89}{space 3} .1955493
{txt}{space 34} {c |}
{space 5}c.ctreatmentwidth9#c.st_ethno {c |}{col 36}{res}{space 2} .6425878{col 48}{space 2} .1423816{col 59}{space 1}    4.51{col 68}{space 3}0.000{col 76}{space 4} .3607522{col 89}{space 3} .9244235
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0016432{col 48}{space 2} .0357264{col 59}{space 1}   -0.05{col 68}{space 3}0.963{col 76}{space 4}-.0723615{col 89}{space 3} .0690751
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6435442{col 48}{space 2} .3167676{col 59}{space 1}    2.03{col 68}{space 3}0.044{col 76}{space 4} .0165222{col 89}{space 3} 1.270566
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5367967{col 48}{space 2} .3581797{col 59}{space 1}   -1.50{col 68}{space 3}0.137{col 76}{space 4}-1.245791{col 89}{space 3}  .172198
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1067489{col 48}{space 2} .0646413{col 59}{space 1}   -1.65{col 68}{space 3}0.101{col 76}{space 4}-.2347023{col 89}{space 3} .0212046
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0296422{col 48}{space 2} .0782318{col 59}{space 1}   -0.38{col 68}{space 3}0.705{col 76}{space 4}-.1844973{col 89}{space 3} .1252128
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1197977{col 48}{space 2} .0747046{col 59}{space 1}   -1.60{col 68}{space 3}0.111{col 76}{space 4}-.2676708{col 89}{space 3} .0280754
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0773139{col 48}{space 2} .0729377{col 59}{space 1}   -1.06{col 68}{space 3}0.291{col 76}{space 4}-.2216895{col 89}{space 3} .0670618
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1331966{col 48}{space 2} .0864984{col 59}{space 1}   -1.54{col 68}{space 3}0.126{col 76}{space 4}-.3044149{col 89}{space 3} .0380218
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0049908{col 48}{space 2} .1118859{col 59}{space 1}   -0.04{col 68}{space 3}0.964{col 76}{space 4}-.2264621{col 89}{space 3} .2164804
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0983701{col 48}{space 2} .1167611{col 59}{space 1}   -0.84{col 68}{space 3}0.401{col 76}{space 4}-.3294916{col 89}{space 3} .1327515
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5576879{col 48}{space 2} .1046945{col 59}{space 1}    5.33{col 68}{space 3}0.000{col 76}{space 4} .3504515{col 89}{space 3} .7649242
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       269
{txt}{col 1}Number of PSUs{col 20}= {res}      125{txt}{col 49}Population size{col 67}={res} 266.755062
{txt}{col 49}Design df{col 67}= {res}       124
{txt}{col 49}F({res}  13{txt},{res}    112{txt}){col 67}= {res}      4.50
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1286

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth10 {c |}{col 36}{res}{space 2} -.172757{col 48}{space 2} .0659749{col 59}{space 1}   -2.62{col 68}{space 3}0.010{col 76}{space 4}-.3033399{col 89}{space 3}-.0421741
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0031275{col 48}{space 2} .0971012{col 59}{space 1}    0.03{col 68}{space 3}0.974{col 76}{space 4} -.189063{col 89}{space 3} .1953179
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth10#c.st_ethno {c |}{col 36}{res}{space 2} .6527013{col 48}{space 2}  .141956{col 59}{space 1}    4.60{col 68}{space 3}0.000{col 76}{space 4} .3717305{col 89}{space 3} .9336721
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0015011{col 48}{space 2} .0351954{col 59}{space 1}   -0.04{col 68}{space 3}0.966{col 76}{space 4}-.0711627{col 89}{space 3} .0681604
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6268613{col 48}{space 2} .3158407{col 59}{space 1}    1.98{col 68}{space 3}0.049{col 76}{space 4}  .001724{col 89}{space 3} 1.251998
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5254965{col 48}{space 2} .3573209{col 59}{space 1}   -1.47{col 68}{space 3}0.144{col 76}{space 4}-1.232735{col 89}{space 3} .1817417
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1073001{col 48}{space 2} .0650141{col 59}{space 1}   -1.65{col 68}{space 3}0.101{col 76}{space 4}-.2359813{col 89}{space 3} .0213811
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0322782{col 48}{space 2} .0778097{col 59}{space 1}   -0.41{col 68}{space 3}0.679{col 76}{space 4}-.1862855{col 89}{space 3}  .121729
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1160877{col 48}{space 2}  .074569{col 59}{space 1}   -1.56{col 68}{space 3}0.122{col 76}{space 4}-.2636807{col 89}{space 3} .0315053
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} -.076613{col 48}{space 2}   .07259{col 59}{space 1}   -1.06{col 68}{space 3}0.293{col 76}{space 4}-.2202889{col 89}{space 3} .0670629
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} -.131244{col 48}{space 2} .0862763{col 59}{space 1}   -1.52{col 68}{space 3}0.131{col 76}{space 4}-.3020091{col 89}{space 3} .0395211
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0033789{col 48}{space 2} .1119566{col 59}{space 1}   -0.03{col 68}{space 3}0.976{col 76}{space 4}-.2249724{col 89}{space 3} .2182145
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1082515{col 48}{space 2} .1122091{col 59}{space 1}   -0.96{col 68}{space 3}0.337{col 76}{space 4}-.3303446{col 89}{space 3} .1138417
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5615432{col 48}{space 2} .1044762{col 59}{space 1}    5.37{col 68}{space 3}0.000{col 76}{space 4} .3547556{col 89}{space 3} .7683308
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       271
{txt}{col 1}Number of PSUs{col 20}= {res}      125{txt}{col 49}Population size{col 67}={res} 269.222716
{txt}{col 49}Design df{col 67}= {res}       124
{txt}{col 49}F({res}  13{txt},{res}    112{txt}){col 67}= {res}      4.43
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1256

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth11 {c |}{col 36}{res}{space 2}-.1510286{col 48}{space 2} .0658753{col 59}{space 1}   -2.29{col 68}{space 3}0.024{col 76}{space 4}-.2814143{col 89}{space 3}-.0206429
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0026436{col 48}{space 2} .0970066{col 59}{space 1}    0.03{col 68}{space 3}0.978{col 76}{space 4}-.1893596{col 89}{space 3} .1946468
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth11#c.st_ethno {c |}{col 36}{res}{space 2} .6017902{col 48}{space 2} .1406234{col 59}{space 1}    4.28{col 68}{space 3}0.000{col 76}{space 4} .3234571{col 89}{space 3} .8801234
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0018075{col 48}{space 2} .0351196{col 59}{space 1}   -0.05{col 68}{space 3}0.959{col 76}{space 4} -.071319{col 89}{space 3} .0677039
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6255109{col 48}{space 2} .3151873{col 59}{space 1}    1.98{col 68}{space 3}0.049{col 76}{space 4}  .001667{col 89}{space 3} 1.249355
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5309743{col 48}{space 2} .3563824{col 59}{space 1}   -1.49{col 68}{space 3}0.139{col 76}{space 4}-1.236355{col 89}{space 3} .1744063
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1083605{col 48}{space 2} .0648741{col 59}{space 1}   -1.67{col 68}{space 3}0.097{col 76}{space 4}-.2367645{col 89}{space 3} .0200435
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0361327{col 48}{space 2} .0778631{col 59}{space 1}   -0.46{col 68}{space 3}0.643{col 76}{space 4}-.1902456{col 89}{space 3} .1179802
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1191615{col 48}{space 2} .0750325{col 59}{space 1}   -1.59{col 68}{space 3}0.115{col 76}{space 4}-.2676718{col 89}{space 3} .0293489
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0745852{col 48}{space 2} .0731192{col 59}{space 1}   -1.02{col 68}{space 3}0.310{col 76}{space 4}-.2193085{col 89}{space 3} .0701382
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1258175{col 48}{space 2} .0866431{col 59}{space 1}   -1.45{col 68}{space 3}0.149{col 76}{space 4}-.2973084{col 89}{space 3} .0456734
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0028985{col 48}{space 2} .1123744{col 59}{space 1}   -0.03{col 68}{space 3}0.979{col 76}{space 4}-.2253189{col 89}{space 3} .2195218
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1088308{col 48}{space 2} .1123465{col 59}{space 1}   -0.97{col 68}{space 3}0.335{col 76}{space 4} -.331196{col 89}{space 3} .1135344
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5630279{col 48}{space 2}  .104117{col 59}{space 1}    5.41{col 68}{space 3}0.000{col 76}{space 4} .3569512{col 89}{space 3} .7691047
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       271
{txt}{col 1}Number of PSUs{col 20}= {res}      125{txt}{col 49}Population size{col 67}={res} 269.222716
{txt}{col 49}Design df{col 67}= {res}       124
{txt}{col 49}F({res}  13{txt},{res}    112{txt}){col 67}= {res}      4.43
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1256

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth12 {c |}{col 36}{res}{space 2}-.1510286{col 48}{space 2} .0658753{col 59}{space 1}   -2.29{col 68}{space 3}0.024{col 76}{space 4}-.2814143{col 89}{space 3}-.0206429
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0026436{col 48}{space 2} .0970066{col 59}{space 1}    0.03{col 68}{space 3}0.978{col 76}{space 4}-.1893596{col 89}{space 3} .1946468
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth12#c.st_ethno {c |}{col 36}{res}{space 2} .6017902{col 48}{space 2} .1406234{col 59}{space 1}    4.28{col 68}{space 3}0.000{col 76}{space 4} .3234571{col 89}{space 3} .8801234
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0018075{col 48}{space 2} .0351196{col 59}{space 1}   -0.05{col 68}{space 3}0.959{col 76}{space 4} -.071319{col 89}{space 3} .0677039
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6255109{col 48}{space 2} .3151873{col 59}{space 1}    1.98{col 68}{space 3}0.049{col 76}{space 4}  .001667{col 89}{space 3} 1.249355
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5309743{col 48}{space 2} .3563824{col 59}{space 1}   -1.49{col 68}{space 3}0.139{col 76}{space 4}-1.236355{col 89}{space 3} .1744063
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1083605{col 48}{space 2} .0648741{col 59}{space 1}   -1.67{col 68}{space 3}0.097{col 76}{space 4}-.2367645{col 89}{space 3} .0200435
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0361327{col 48}{space 2} .0778631{col 59}{space 1}   -0.46{col 68}{space 3}0.643{col 76}{space 4}-.1902456{col 89}{space 3} .1179802
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1191615{col 48}{space 2} .0750325{col 59}{space 1}   -1.59{col 68}{space 3}0.115{col 76}{space 4}-.2676718{col 89}{space 3} .0293489
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0745852{col 48}{space 2} .0731192{col 59}{space 1}   -1.02{col 68}{space 3}0.310{col 76}{space 4}-.2193085{col 89}{space 3} .0701382
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1258175{col 48}{space 2} .0866431{col 59}{space 1}   -1.45{col 68}{space 3}0.149{col 76}{space 4}-.2973084{col 89}{space 3} .0456734
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0028985{col 48}{space 2} .1123744{col 59}{space 1}   -0.03{col 68}{space 3}0.979{col 76}{space 4}-.2253189{col 89}{space 3} .2195218
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1088308{col 48}{space 2} .1123465{col 59}{space 1}   -0.97{col 68}{space 3}0.335{col 76}{space 4} -.331196{col 89}{space 3} .1135344
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5630279{col 48}{space 2}  .104117{col 59}{space 1}    5.41{col 68}{space 3}0.000{col 76}{space 4} .3569512{col 89}{space 3} .7691047
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       280
{txt}{col 1}Number of PSUs{col 20}= {res}      126{txt}{col 49}Population size{col 67}={res} 278.926203
{txt}{col 49}Design df{col 67}= {res}       125
{txt}{col 49}F({res}  13{txt},{res}    113{txt}){col 67}= {res}      5.15
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1270

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth13 {c |}{col 36}{res}{space 2}-.1573817{col 48}{space 2} .0646181{col 59}{space 1}   -2.44{col 68}{space 3}0.016{col 76}{space 4} -.285269{col 89}{space 3}-.0294944
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0064702{col 48}{space 2} .0966227{col 59}{space 1}    0.07{col 68}{space 3}0.947{col 76}{space 4}-.1847581{col 89}{space 3} .1976986
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth13#c.st_ethno {c |}{col 36}{res}{space 2} .5914696{col 48}{space 2}  .132838{col 59}{space 1}    4.45{col 68}{space 3}0.000{col 76}{space 4} .3285667{col 89}{space 3} .8543726
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0051311{col 48}{space 2} .0339359{col 59}{space 1}    0.15{col 68}{space 3}0.880{col 76}{space 4}-.0620323{col 89}{space 3} .0722945
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5973961{col 48}{space 2} .3136581{col 59}{space 1}    1.90{col 68}{space 3}0.059{col 76}{space 4}-.0233723{col 89}{space 3} 1.218164
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5195629{col 48}{space 2} .3536768{col 59}{space 1}   -1.47{col 68}{space 3}0.144{col 76}{space 4}-1.219533{col 89}{space 3} .1804074
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1079752{col 48}{space 2} .0621418{col 59}{space 1}   -1.74{col 68}{space 3}0.085{col 76}{space 4}-.2309615{col 89}{space 3} .0150111
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0556504{col 48}{space 2} .0745713{col 59}{space 1}   -0.75{col 68}{space 3}0.457{col 76}{space 4}-.2032363{col 89}{space 3} .0919355
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1167394{col 48}{space 2} .0722429{col 59}{space 1}   -1.62{col 68}{space 3}0.109{col 76}{space 4} -.259717{col 89}{space 3} .0262381
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0701096{col 48}{space 2} .0725094{col 59}{space 1}   -0.97{col 68}{space 3}0.335{col 76}{space 4}-.2136148{col 89}{space 3} .0733955
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1206834{col 48}{space 2} .0858651{col 59}{space 1}   -1.41{col 68}{space 3}0.162{col 76}{space 4} -.290621{col 89}{space 3} .0492542
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0009936{col 48}{space 2} .1068688{col 59}{space 1}   -0.01{col 68}{space 3}0.993{col 76}{space 4}-.2125003{col 89}{space 3}  .210513
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1079403{col 48}{space 2} .1127759{col 59}{space 1}   -0.96{col 68}{space 3}0.340{col 76}{space 4}-.3311377{col 89}{space 3} .1152571
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5674065{col 48}{space 2} .1034799{col 59}{space 1}    5.48{col 68}{space 3}0.000{col 76}{space 4} .3626069{col 89}{space 3} .7722061
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       288
{txt}{col 1}Number of PSUs{col 20}= {res}      130{txt}{col 49}Population size{col 67}={res} 286.695387
{txt}{col 49}Design df{col 67}= {res}       129
{txt}{col 49}F({res}  13{txt},{res}    117{txt}){col 67}= {res}      4.45
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1156

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth14 {c |}{col 36}{res}{space 2}-.1628348{col 48}{space 2} .0655079{col 59}{space 1}   -2.49{col 68}{space 3}0.014{col 76}{space 4}-.2924437{col 89}{space 3}-.0332259
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0114443{col 48}{space 2} .0970738{col 59}{space 1}    0.12{col 68}{space 3}0.906{col 76}{space 4}-.1806187{col 89}{space 3} .2035072
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth14#c.st_ethno {c |}{col 36}{res}{space 2} .5655171{col 48}{space 2} .1367022{col 59}{space 1}    4.14{col 68}{space 3}0.000{col 76}{space 4} .2950484{col 89}{space 3} .8359857
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0078062{col 48}{space 2} .0332015{col 59}{space 1}    0.24{col 68}{space 3}0.814{col 76}{space 4}-.0578839{col 89}{space 3} .0734963
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5346837{col 48}{space 2}  .305654{col 59}{space 1}    1.75{col 68}{space 3}0.083{col 76}{space 4}-.0700601{col 89}{space 3} 1.139428
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.4802435{col 48}{space 2} .3460877{col 59}{space 1}   -1.39{col 68}{space 3}0.168{col 76}{space 4}-1.164987{col 89}{space 3} .2044995
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1132656{col 48}{space 2} .0628334{col 59}{space 1}   -1.80{col 68}{space 3}0.074{col 76}{space 4}-.2375831{col 89}{space 3} .0110519
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0629306{col 48}{space 2} .0742658{col 59}{space 1}   -0.85{col 68}{space 3}0.398{col 76}{space 4}-.2098673{col 89}{space 3} .0840061
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1115711{col 48}{space 2} .0723227{col 59}{space 1}   -1.54{col 68}{space 3}0.125{col 76}{space 4}-.2546633{col 89}{space 3} .0315212
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0620215{col 48}{space 2} .0692967{col 59}{space 1}   -0.90{col 68}{space 3}0.372{col 76}{space 4}-.1991267{col 89}{space 3} .0750837
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0999153{col 48}{space 2} .0836714{col 59}{space 1}   -1.19{col 68}{space 3}0.235{col 76}{space 4}-.2654613{col 89}{space 3} .0656307
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0031054{col 48}{space 2} .1001733{col 59}{space 1}   -0.03{col 68}{space 3}0.975{col 76}{space 4}-.2013007{col 89}{space 3} .1950898
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1055014{col 48}{space 2} .1104986{col 59}{space 1}   -0.95{col 68}{space 3}0.341{col 76}{space 4}-.3241257{col 89}{space 3} .1131228
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .574099{col 48}{space 2} .1015397{col 59}{space 1}    5.65{col 68}{space 3}0.000{col 76}{space 4} .3732003{col 89}{space 3} .7749977
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       291
{txt}{col 1}Number of PSUs{col 20}= {res}      131{txt}{col 49}Population size{col 67}={res} 289.696391
{txt}{col 49}Design df{col 67}= {res}       130
{txt}{col 49}F({res}  13{txt},{res}    118{txt}){col 67}= {res}      4.66
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1224

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth15 {c |}{col 36}{res}{space 2}-.1807618{col 48}{space 2} .0640704{col 59}{space 1}   -2.82{col 68}{space 3}0.006{col 76}{space 4}-.3075174{col 89}{space 3}-.0540062
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0164152{col 48}{space 2} .0968984{col 59}{space 1}    0.17{col 68}{space 3}0.866{col 76}{space 4}-.1752867{col 89}{space 3}  .208117
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth15#c.st_ethno {c |}{col 36}{res}{space 2} .5995316{col 48}{space 2} .1327279{col 59}{space 1}    4.52{col 68}{space 3}0.000{col 76}{space 4} .3369455{col 89}{space 3} .8621178
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0109913{col 48}{space 2} .0332612{col 59}{space 1}    0.33{col 68}{space 3}0.742{col 76}{space 4} -.054812{col 89}{space 3} .0767945
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5187844{col 48}{space 2} .3047821{col 59}{space 1}    1.70{col 68}{space 3}0.091{col 76}{space 4}-.0841905{col 89}{space 3} 1.121759
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.4604407{col 48}{space 2} .3447135{col 59}{space 1}   -1.34{col 68}{space 3}0.184{col 76}{space 4}-1.142415{col 89}{space 3} .2215337
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} -.112978{col 48}{space 2} .0622057{col 59}{space 1}   -1.82{col 68}{space 3}0.072{col 76}{space 4}-.2360446{col 89}{space 3} .0100887
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}  -.05934{col 48}{space 2} .0740676{col 59}{space 1}   -0.80{col 68}{space 3}0.425{col 76}{space 4}-.2058738{col 89}{space 3} .0871939
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1073185{col 48}{space 2} .0717226{col 59}{space 1}   -1.50{col 68}{space 3}0.137{col 76}{space 4} -.249213{col 89}{space 3} .0345761
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0598077{col 48}{space 2} .0688568{col 59}{space 1}   -0.87{col 68}{space 3}0.387{col 76}{space 4}-.1960327{col 89}{space 3} .0764173
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1082491{col 48}{space 2} .0827081{col 59}{space 1}   -1.31{col 68}{space 3}0.193{col 76}{space 4}-.2718772{col 89}{space 3} .0553791
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} -.003824{col 48}{space 2} .0999084{col 59}{space 1}   -0.04{col 68}{space 3}0.970{col 76}{space 4}-.2014809{col 89}{space 3} .1938329
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1064242{col 48}{space 2} .1104767{col 59}{space 1}   -0.96{col 68}{space 3}0.337{col 76}{space 4}-.3249891{col 89}{space 3} .1121407
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5729709{col 48}{space 2} .1013026{col 59}{space 1}    5.66{col 68}{space 3}0.000{col 76}{space 4} .3725558{col 89}{space 3} .7733859
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       297
{txt}{col 1}Number of PSUs{col 20}= {res}      131{txt}{col 49}Population size{col 67}={res} 294.997921
{txt}{col 49}Design df{col 67}= {res}       130
{txt}{col 49}F({res}  13{txt},{res}    118{txt}){col 67}= {res}      4.89
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1259

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth16 {c |}{col 36}{res}{space 2}-.1859882{col 48}{space 2}  .061894{col 59}{space 1}   -3.00{col 68}{space 3}0.003{col 76}{space 4} -.308438{col 89}{space 3}-.0635383
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0195903{col 48}{space 2} .0969148{col 59}{space 1}    0.20{col 68}{space 3}0.840{col 76}{space 4}-.1721441{col 89}{space 3} .2113246
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth16#c.st_ethno {c |}{col 36}{res}{space 2} .6026081{col 48}{space 2} .1289083{col 59}{space 1}    4.67{col 68}{space 3}0.000{col 76}{space 4} .3475783{col 89}{space 3} .8576378
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0133322{col 48}{space 2} .0329499{col 59}{space 1}    0.40{col 68}{space 3}0.686{col 76}{space 4}-.0518552{col 89}{space 3} .0785197
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4959947{col 48}{space 2} .3002978{col 59}{space 1}    1.65{col 68}{space 3}0.101{col 76}{space 4}-.0981085{col 89}{space 3} 1.090098
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.4353784{col 48}{space 2} .3424527{col 59}{space 1}   -1.27{col 68}{space 3}0.206{col 76}{space 4} -1.11288{col 89}{space 3} .2421233
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1146767{col 48}{space 2} .0623528{col 59}{space 1}   -1.84{col 68}{space 3}0.068{col 76}{space 4}-.2380343{col 89}{space 3} .0086809
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0564643{col 48}{space 2} .0737806{col 59}{space 1}   -0.77{col 68}{space 3}0.445{col 76}{space 4}-.2024304{col 89}{space 3} .0895018
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}  -.10631{col 48}{space 2}  .071419{col 59}{space 1}   -1.49{col 68}{space 3}0.139{col 76}{space 4} -.247604{col 89}{space 3} .0349839
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0587176{col 48}{space 2}  .068659{col 59}{space 1}   -0.86{col 68}{space 3}0.394{col 76}{space 4}-.1945512{col 89}{space 3} .0771161
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1123664{col 48}{space 2} .0819717{col 59}{space 1}   -1.37{col 68}{space 3}0.173{col 76}{space 4}-.2745377{col 89}{space 3} .0498048
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0034894{col 48}{space 2} .0967713{col 59}{space 1}    0.04{col 68}{space 3}0.971{col 76}{space 4} -.187961{col 89}{space 3} .1949398
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1086004{col 48}{space 2} .1100389{col 59}{space 1}   -0.99{col 68}{space 3}0.326{col 76}{space 4}-.3262991{col 89}{space 3} .1090984
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5752197{col 48}{space 2} .1010822{col 59}{space 1}    5.69{col 68}{space 3}0.000{col 76}{space 4} .3752406{col 89}{space 3} .7751988
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       305
{txt}{col 1}Number of PSUs{col 20}= {res}      132{txt}{col 49}Population size{col 67}={res} 303.467582
{txt}{col 49}Design df{col 67}= {res}       131
{txt}{col 49}F({res}  13{txt},{res}    119{txt}){col 67}= {res}      2.32
{txt}{col 49}Prob > F{col 67}= {res}    0.0089
{txt}{col 49}R-squared{col 67}= {res}    0.0961

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth17 {c |}{col 36}{res}{space 2} -.133063{col 48}{space 2} .0671159{col 59}{space 1}   -1.98{col 68}{space 3}0.050{col 76}{space 4}-.2658343{col 89}{space 3}-.0002917
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0183837{col 48}{space 2} .0978371{col 59}{space 1}    0.19{col 68}{space 3}0.851{col 76}{space 4}-.1751613{col 89}{space 3} .2119288
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth17#c.st_ethno {c |}{col 36}{res}{space 2} .4412679{col 48}{space 2} .1512689{col 59}{space 1}    2.92{col 68}{space 3}0.004{col 76}{space 4} .1420219{col 89}{space 3} .7405138
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0107338{col 48}{space 2} .0326713{col 59}{space 1}    0.33{col 68}{space 3}0.743{col 76}{space 4}-.0538978{col 89}{space 3} .0753653
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4855447{col 48}{space 2} .2992219{col 59}{space 1}    1.62{col 68}{space 3}0.107{col 76}{space 4}-.1063875{col 89}{space 3} 1.077477
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.4297587{col 48}{space 2} .3463492{col 59}{space 1}   -1.24{col 68}{space 3}0.217{col 76}{space 4} -1.11492{col 89}{space 3} .2554027
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1262753{col 48}{space 2} .0638802{col 59}{space 1}   -1.98{col 68}{space 3}0.050{col 76}{space 4}-.2526455{col 89}{space 3} .0000949
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0770756{col 48}{space 2} .0746259{col 59}{space 1}   -1.03{col 68}{space 3}0.304{col 76}{space 4}-.2247035{col 89}{space 3} .0705523
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1156144{col 48}{space 2} .0729042{col 59}{space 1}   -1.59{col 68}{space 3}0.115{col 76}{space 4}-.2598362{col 89}{space 3} .0286074
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0698186{col 48}{space 2} .0649165{col 59}{space 1}   -1.08{col 68}{space 3}0.284{col 76}{space 4}-.1982388{col 89}{space 3} .0586016
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1247416{col 48}{space 2} .0790073{col 59}{space 1}   -1.58{col 68}{space 3}0.117{col 76}{space 4}-.2810369{col 89}{space 3} .0315537
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0026284{col 48}{space 2} .0926717{col 59}{space 1}   -0.03{col 68}{space 3}0.977{col 76}{space 4}-.1859551{col 89}{space 3} .1806984
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1218168{col 48}{space 2} .1079922{col 59}{space 1}   -1.13{col 68}{space 3}0.261{col 76}{space 4}-.3354512{col 89}{space 3} .0918176
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6029221{col 48}{space 2} .1015051{col 59}{space 1}    5.94{col 68}{space 3}0.000{col 76}{space 4} .4021207{col 89}{space 3} .8037235
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       307
{txt}{col 1}Number of PSUs{col 20}= {res}      133{txt}{col 49}Population size{col 67}={res} 305.234758
{txt}{col 49}Design df{col 67}= {res}       132
{txt}{col 49}F({res}  13{txt},{res}    120{txt}){col 67}= {res}      2.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0074
{txt}{col 49}R-squared{col 67}= {res}    0.0964

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth18 {c |}{col 36}{res}{space 2}-.1322246{col 48}{space 2} .0669934{col 59}{space 1}   -1.97{col 68}{space 3}0.051{col 76}{space 4}-.2647441{col 89}{space 3} .0002949
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0189996{col 48}{space 2} .0977715{col 59}{space 1}    0.19{col 68}{space 3}0.846{col 76}{space 4}-.1744021{col 89}{space 3} .2124013
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth18#c.st_ethno {c |}{col 36}{res}{space 2} .4372295{col 48}{space 2} .1513187{col 59}{space 1}    2.89{col 68}{space 3}0.005{col 76}{space 4} .1379062{col 89}{space 3} .7365529
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}  .014718{col 48}{space 2} .0326549{col 59}{space 1}    0.45{col 68}{space 3}0.653{col 76}{space 4}-.0498765{col 89}{space 3} .0793126
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4709306{col 48}{space 2} .2993552{col 59}{space 1}    1.57{col 68}{space 3}0.118{col 76}{space 4}-.1212235{col 89}{space 3} 1.063085
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.3936028{col 48}{space 2} .3473047{col 59}{space 1}   -1.13{col 68}{space 3}0.259{col 76}{space 4}-1.080606{col 89}{space 3} .2934002
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1280019{col 48}{space 2} .0637574{col 59}{space 1}   -2.01{col 68}{space 3}0.047{col 76}{space 4}-.2541204{col 89}{space 3}-.0018835
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0732092{col 48}{space 2}  .074259{col 59}{space 1}   -0.99{col 68}{space 3}0.326{col 76}{space 4}-.2201008{col 89}{space 3} .0736824
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1152489{col 48}{space 2} .0728744{col 59}{space 1}   -1.58{col 68}{space 3}0.116{col 76}{space 4}-.2594016{col 89}{space 3} .0289038
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0683965{col 48}{space 2} .0654205{col 59}{space 1}   -1.05{col 68}{space 3}0.298{col 76}{space 4}-.1978047{col 89}{space 3} .0610118
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1341641{col 48}{space 2} .0792167{col 59}{space 1}   -1.69{col 68}{space 3}0.093{col 76}{space 4}-.2908627{col 89}{space 3} .0225344
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0045945{col 48}{space 2}  .092748{col 59}{space 1}   -0.05{col 68}{space 3}0.961{col 76}{space 4}-.1880592{col 89}{space 3} .1788702
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1219586{col 48}{space 2} .1080263{col 59}{space 1}   -1.13{col 68}{space 3}0.261{col 76}{space 4}-.3356454{col 89}{space 3} .0917282
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6009367{col 48}{space 2} .1017359{col 59}{space 1}    5.91{col 68}{space 3}0.000{col 76}{space 4} .3996931{col 89}{space 3} .8021803
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       308
{txt}{col 1}Number of PSUs{col 20}= {res}      133{txt}{col 49}Population size{col 67}={res} 306.468585
{txt}{col 49}Design df{col 67}= {res}       132
{txt}{col 49}F({res}  13{txt},{res}    120{txt}){col 67}= {res}      2.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0074
{txt}{col 49}R-squared{col 67}= {res}    0.0964

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth19 {c |}{col 36}{res}{space 2}-.1323665{col 48}{space 2} .0670261{col 59}{space 1}   -1.97{col 68}{space 3}0.050{col 76}{space 4}-.2649508{col 89}{space 3} .0002178
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0188128{col 48}{space 2} .0978605{col 59}{space 1}    0.19{col 68}{space 3}0.848{col 76}{space 4} -.174765{col 89}{space 3} .2123907
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth19#c.st_ethno {c |}{col 36}{res}{space 2} .4371919{col 48}{space 2} .1512869{col 59}{space 1}    2.89{col 68}{space 3}0.005{col 76}{space 4} .1379315{col 89}{space 3} .7364522
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0145321{col 48}{space 2} .0324234{col 59}{space 1}    0.45{col 68}{space 3}0.655{col 76}{space 4}-.0496046{col 89}{space 3} .0786689
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .4733082{col 48}{space 2}  .293275{col 59}{space 1}    1.61{col 68}{space 3}0.109{col 76}{space 4}-.1068187{col 89}{space 3} 1.053435
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.3955914{col 48}{space 2} .3430958{col 59}{space 1}   -1.15{col 68}{space 3}0.251{col 76}{space 4}-1.074269{col 89}{space 3}  .283086
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} -.128099{col 48}{space 2} .0637412{col 59}{space 1}   -2.01{col 68}{space 3}0.047{col 76}{space 4}-.2541855{col 89}{space 3}-.0020126
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} -.073601{col 48}{space 2}   .07371{col 59}{space 1}   -1.00{col 68}{space 3}0.320{col 76}{space 4}-.2194067{col 89}{space 3} .0722048
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} -.115378{col 48}{space 2} .0728447{col 59}{space 1}   -1.58{col 68}{space 3}0.116{col 76}{space 4}-.2594719{col 89}{space 3}  .028716
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0684994{col 48}{space 2} .0653605{col 59}{space 1}   -1.05{col 68}{space 3}0.297{col 76}{space 4}-.1977889{col 89}{space 3} .0607901
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1343252{col 48}{space 2} .0791653{col 59}{space 1}   -1.70{col 68}{space 3}0.092{col 76}{space 4}-.2909221{col 89}{space 3} .0222716
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0045772{col 48}{space 2} .0927525{col 59}{space 1}   -0.05{col 68}{space 3}0.961{col 76}{space 4}-.1880509{col 89}{space 3} .1788965
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1215727{col 48}{space 2} .1075246{col 59}{space 1}   -1.13{col 68}{space 3}0.260{col 76}{space 4} -.334267{col 89}{space 3} .0911216
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6007718{col 48}{space 2} .1015877{col 59}{space 1}    5.91{col 68}{space 3}0.000{col 76}{space 4} .3998213{col 89}{space 3} .8017224
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       313
{txt}{col 1}Number of PSUs{col 20}= {res}      133{txt}{col 49}Population size{col 67}={res} 311.236765
{txt}{col 49}Design df{col 67}= {res}       132
{txt}{col 49}F({res}  13{txt},{res}    120{txt}){col 67}= {res}      2.39
{txt}{col 49}Prob > F{col 67}= {res}    0.0069
{txt}{col 49}R-squared{col 67}= {res}    0.0956

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth20 {c |}{col 36}{res}{space 2}-.1302549{col 48}{space 2} .0649068{col 59}{space 1}   -2.01{col 68}{space 3}0.047{col 76}{space 4}-.2586469{col 89}{space 3}-.0018629
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0224355{col 48}{space 2} .0979062{col 59}{space 1}    0.23{col 68}{space 3}0.819{col 76}{space 4}-.1712326{col 89}{space 3} .2161035
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth20#c.st_ethno {c |}{col 36}{res}{space 2} .4383751{col 48}{space 2} .1474247{col 59}{space 1}    2.97{col 68}{space 3}0.004{col 76}{space 4} .1467545{col 89}{space 3} .7299958
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0184001{col 48}{space 2} .0321314{col 59}{space 1}    0.57{col 68}{space 3}0.568{col 76}{space 4}-.0451591{col 89}{space 3} .0819592
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .3949104{col 48}{space 2} .2882901{col 59}{space 1}    1.37{col 68}{space 3}0.173{col 76}{space 4}-.1753559{col 89}{space 3} .9651768
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.3244173{col 48}{space 2} .3404131{col 59}{space 1}   -0.95{col 68}{space 3}0.342{col 76}{space 4} -.997788{col 89}{space 3} .3489533
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1225905{col 48}{space 2} .0638067{col 59}{space 1}   -1.92{col 68}{space 3}0.057{col 76}{space 4}-.2488065{col 89}{space 3} .0036255
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0713976{col 48}{space 2} .0738358{col 59}{space 1}   -0.97{col 68}{space 3}0.335{col 76}{space 4}-.2174521{col 89}{space 3}  .074657
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1135646{col 48}{space 2} .0726109{col 59}{space 1}   -1.56{col 68}{space 3}0.120{col 76}{space 4}-.2571961{col 89}{space 3}  .030067
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0648829{col 48}{space 2} .0645309{col 59}{space 1}   -1.01{col 68}{space 3}0.317{col 76}{space 4}-.1925313{col 89}{space 3} .0627656
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1300524{col 48}{space 2} .0781884{col 59}{space 1}   -1.66{col 68}{space 3}0.099{col 76}{space 4}-.2847167{col 89}{space 3} .0246119
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} -.003866{col 48}{space 2} .0922662{col 59}{space 1}   -0.04{col 68}{space 3}0.967{col 76}{space 4}-.1863776{col 89}{space 3} .1786456
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1312006{col 48}{space 2} .1063102{col 59}{space 1}   -1.23{col 68}{space 3}0.219{col 76}{space 4}-.3414928{col 89}{space 3} .0790916
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6084415{col 48}{space 2} .1004133{col 59}{space 1}    6.06{col 68}{space 3}0.000{col 76}{space 4}  .409814{col 89}{space 3} .8070689
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       317
{txt}{col 1}Number of PSUs{col 20}= {res}      134{txt}{col 49}Population size{col 67}={res} 315.471596
{txt}{col 49}Design df{col 67}= {res}       133
{txt}{col 49}F({res}  13{txt},{res}    121{txt}){col 67}= {res}      2.49
{txt}{col 49}Prob > F{col 67}= {res}    0.0049
{txt}{col 49}R-squared{col 67}= {res}    0.0946

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth21 {c |}{col 36}{res}{space 2}-.1277762{col 48}{space 2} .0625224{col 59}{space 1}   -2.04{col 68}{space 3}0.043{col 76}{space 4}-.2514432{col 89}{space 3}-.0041092
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0251432{col 48}{space 2} .0982915{col 59}{space 1}    0.26{col 68}{space 3}0.798{col 76}{space 4}-.1692735{col 89}{space 3} .2195599
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth21#c.st_ethno {c |}{col 36}{res}{space 2} .4323484{col 48}{space 2} .1435578{col 59}{space 1}    3.01{col 68}{space 3}0.003{col 76}{space 4} .1483965{col 89}{space 3} .7163002
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}  .019471{col 48}{space 2} .0316827{col 59}{space 1}    0.61{col 68}{space 3}0.540{col 76}{space 4}-.0431961{col 89}{space 3} .0821381
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .366354{col 48}{space 2} .2862034{col 59}{space 1}    1.28{col 68}{space 3}0.203{col 76}{space 4}-.1997453{col 89}{space 3} .9324532
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.3003751{col 48}{space 2} .3378377{col 59}{space 1}   -0.89{col 68}{space 3}0.376{col 76}{space 4} -.968605{col 89}{space 3} .3678548
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1242669{col 48}{space 2} .0637991{col 59}{space 1}   -1.95{col 68}{space 3}0.054{col 76}{space 4} -.250459{col 89}{space 3} .0019252
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} -.070696{col 48}{space 2} .0739654{col 59}{space 1}   -0.96{col 68}{space 3}0.341{col 76}{space 4}-.2169966{col 89}{space 3} .0756047
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1066762{col 48}{space 2} .0731721{col 59}{space 1}   -1.46{col 68}{space 3}0.147{col 76}{space 4}-.2514078{col 89}{space 3} .0380553
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0646403{col 48}{space 2} .0644661{col 59}{space 1}   -1.00{col 68}{space 3}0.318{col 76}{space 4}-.1921517{col 89}{space 3} .0628711
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1283906{col 48}{space 2} .0788608{col 59}{space 1}   -1.63{col 68}{space 3}0.106{col 76}{space 4}-.2843743{col 89}{space 3} .0275931
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0028276{col 48}{space 2} .0924097{col 59}{space 1}   -0.03{col 68}{space 3}0.976{col 76}{space 4}-.1856104{col 89}{space 3} .1799552
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1357119{col 48}{space 2} .1045139{col 59}{space 1}   -1.30{col 68}{space 3}0.196{col 76}{space 4}-.3424363{col 89}{space 3} .0710125
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6122163{col 48}{space 2} .1003524{col 59}{space 1}    6.10{col 68}{space 3}0.000{col 76}{space 4} .4137231{col 89}{space 3} .8107096
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       321
{txt}{col 1}Number of PSUs{col 20}= {res}      135{txt}{col 49}Population size{col 67}={res} 319.706426
{txt}{col 49}Design df{col 67}= {res}       134
{txt}{col 49}F({res}  13{txt},{res}    122{txt}){col 67}= {res}      2.36
{txt}{col 49}Prob > F{col 67}= {res}    0.0075
{txt}{col 49}R-squared{col 67}= {res}    0.0905

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth22 {c |}{col 36}{res}{space 2}-.1254363{col 48}{space 2} .0616329{col 59}{space 1}   -2.04{col 68}{space 3}0.044{col 76}{space 4}-.2473354{col 89}{space 3}-.0035372
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0240509{col 48}{space 2} .0983579{col 59}{space 1}    0.24{col 68}{space 3}0.807{col 76}{space 4}-.1704838{col 89}{space 3} .2185856
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth22#c.st_ethno {c |}{col 36}{res}{space 2} .4160677{col 48}{space 2} .1424117{col 59}{space 1}    2.92{col 68}{space 3}0.004{col 76}{space 4} .1344022{col 89}{space 3} .6977332
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0217574{col 48}{space 2} .0315112{col 59}{space 1}    0.69{col 68}{space 3}0.491{col 76}{space 4}-.0405664{col 89}{space 3} .0840811
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .3435789{col 48}{space 2} .2831818{col 59}{space 1}    1.21{col 68}{space 3}0.227{col 76}{space 4}-.2165053{col 89}{space 3} .9036632
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2559462{col 48}{space 2} .3376926{col 59}{space 1}   -0.76{col 68}{space 3}0.450{col 76}{space 4}-.9238433{col 89}{space 3} .4119508
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1244684{col 48}{space 2} .0638589{col 59}{space 1}   -1.95{col 68}{space 3}0.053{col 76}{space 4}-.2507701{col 89}{space 3} .0018333
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0779999{col 48}{space 2} .0737236{col 59}{space 1}   -1.06{col 68}{space 3}0.292{col 76}{space 4}-.2238124{col 89}{space 3} .0678125
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1085133{col 48}{space 2} .0734219{col 59}{space 1}   -1.48{col 68}{space 3}0.142{col 76}{space 4} -.253729{col 89}{space 3} .0367024
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0626842{col 48}{space 2}  .064868{col 59}{space 1}   -0.97{col 68}{space 3}0.336{col 76}{space 4}-.1909818{col 89}{space 3} .0656134
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1363328{col 48}{space 2} .0786094{col 59}{space 1}   -1.73{col 68}{space 3}0.085{col 76}{space 4}-.2918085{col 89}{space 3} .0191429
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0026526{col 48}{space 2} .0926929{col 59}{space 1}   -0.03{col 68}{space 3}0.977{col 76}{space 4} -.185983{col 89}{space 3} .1806779
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1341373{col 48}{space 2} .1046336{col 59}{space 1}   -1.28{col 68}{space 3}0.202{col 76}{space 4}-.3410843{col 89}{space 3} .0728097
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6142991{col 48}{space 2} .1002294{col 59}{space 1}    6.13{col 68}{space 3}0.000{col 76}{space 4} .4160629{col 89}{space 3} .8125354
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       327
{txt}{col 1}Number of PSUs{col 20}= {res}      135{txt}{col 49}Population size{col 67}={res} 327.109387
{txt}{col 49}Design df{col 67}= {res}       134
{txt}{col 49}F({res}  13{txt},{res}    122{txt}){col 67}= {res}      2.09
{txt}{col 49}Prob > F{col 67}= {res}    0.0190
{txt}{col 49}R-squared{col 67}= {res}    0.0831

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth23 {c |}{col 36}{res}{space 2} -.130011{col 48}{space 2} .0611419{col 59}{space 1}   -2.13{col 68}{space 3}0.035{col 76}{space 4} -.250939{col 89}{space 3} -.009083
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0321923{col 48}{space 2} .0982378{col 59}{space 1}    0.33{col 68}{space 3}0.744{col 76}{space 4}-.1621048{col 89}{space 3} .2264895
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth23#c.st_ethno {c |}{col 36}{res}{space 2} .4143244{col 48}{space 2} .1437415{col 59}{space 1}    2.88{col 68}{space 3}0.005{col 76}{space 4} .1300288{col 89}{space 3}   .69862
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0337116{col 48}{space 2} .0318812{col 59}{space 1}    1.06{col 68}{space 3}0.292{col 76}{space 4} -.029344{col 89}{space 3} .0967671
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .3523803{col 48}{space 2}  .276642{col 59}{space 1}    1.27{col 68}{space 3}0.205{col 76}{space 4}-.1947693{col 89}{space 3} .8995299
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2899046{col 48}{space 2} .3269591{col 59}{space 1}   -0.89{col 68}{space 3}0.377{col 76}{space 4}-.9365728{col 89}{space 3} .3567637
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0890464{col 48}{space 2} .0688018{col 59}{space 1}   -1.29{col 68}{space 3}0.198{col 76}{space 4}-.2251244{col 89}{space 3} .0470315
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} -.036148{col 48}{space 2} .0807667{col 59}{space 1}   -0.45{col 68}{space 3}0.655{col 76}{space 4}-.1958905{col 89}{space 3} .1235946
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0619487{col 48}{space 2} .0778479{col 59}{space 1}   -0.80{col 68}{space 3}0.428{col 76}{space 4}-.2159183{col 89}{space 3}  .092021
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0703658{col 48}{space 2} .0660617{col 59}{space 1}   -1.07{col 68}{space 3}0.289{col 76}{space 4}-.2010244{col 89}{space 3} .0602928
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1360981{col 48}{space 2} .0795963{col 59}{space 1}   -1.71{col 68}{space 3}0.090{col 76}{space 4}-.2935257{col 89}{space 3} .0213295
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0137737{col 48}{space 2} .0938585{col 59}{space 1}   -0.15{col 68}{space 3}0.884{col 76}{space 4}-.1994095{col 89}{space 3} .1718621
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1417485{col 48}{space 2} .1044897{col 59}{space 1}   -1.36{col 68}{space 3}0.177{col 76}{space 4}-.3484109{col 89}{space 3}  .064914
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5785779{col 48}{space 2} .1032309{col 59}{space 1}    5.60{col 68}{space 3}0.000{col 76}{space 4} .3744052{col 89}{space 3} .7827506
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       329
{txt}{col 1}Number of PSUs{col 20}= {res}      135{txt}{col 49}Population size{col 67}={res} 328.876564
{txt}{col 49}Design df{col 67}= {res}       134
{txt}{col 49}F({res}  13{txt},{res}    122{txt}){col 67}= {res}      2.24
{txt}{col 49}Prob > F{col 67}= {res}    0.0117
{txt}{col 49}R-squared{col 67}= {res}    0.0856

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth24 {c |}{col 36}{res}{space 2} -.132829{col 48}{space 2} .0605755{col 59}{space 1}   -2.19{col 68}{space 3}0.030{col 76}{space 4}-.2526369{col 89}{space 3}-.0130212
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0325756{col 48}{space 2} .0983204{col 59}{space 1}    0.33{col 68}{space 3}0.741{col 76}{space 4}-.1618849{col 89}{space 3} .2270361
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth24#c.st_ethno {c |}{col 36}{res}{space 2} .4184931{col 48}{space 2} .1407473{col 59}{space 1}    2.97{col 68}{space 3}0.003{col 76}{space 4} .1401194{col 89}{space 3} .6968668
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0328344{col 48}{space 2} .0316877{col 59}{space 1}    1.04{col 68}{space 3}0.302{col 76}{space 4}-.0298384{col 89}{space 3} .0955072
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .345477{col 48}{space 2} .2763799{col 59}{space 1}    1.25{col 68}{space 3}0.213{col 76}{space 4}-.2011542{col 89}{space 3} .8921083
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2752204{col 48}{space 2} .3265993{col 59}{space 1}   -0.84{col 68}{space 3}0.401{col 76}{space 4} -.921177{col 89}{space 3} .3707362
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} -.089931{col 48}{space 2} .0687913{col 59}{space 1}   -1.31{col 68}{space 3}0.193{col 76}{space 4}-.2259883{col 89}{space 3} .0461262
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}  -.03627{col 48}{space 2}  .080771{col 59}{space 1}   -0.45{col 68}{space 3}0.654{col 76}{space 4}-.1960209{col 89}{space 3} .1234809
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0620789{col 48}{space 2} .0779184{col 59}{space 1}   -0.80{col 68}{space 3}0.427{col 76}{space 4}-.2161879{col 89}{space 3}   .09203
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0701048{col 48}{space 2} .0660515{col 59}{space 1}   -1.06{col 68}{space 3}0.290{col 76}{space 4}-.2007432{col 89}{space 3} .0605336
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1406604{col 48}{space 2} .0791761{col 59}{space 1}   -1.78{col 68}{space 3}0.078{col 76}{space 4}-.2972569{col 89}{space 3} .0159362
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0135777{col 48}{space 2} .0937795{col 59}{space 1}   -0.14{col 68}{space 3}0.885{col 76}{space 4}-.1990573{col 89}{space 3} .1719018
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1416223{col 48}{space 2} .1045079{col 59}{space 1}   -1.36{col 68}{space 3}0.178{col 76}{space 4}-.3483206{col 89}{space 3} .0650761
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .580125{col 48}{space 2}  .102965{col 59}{space 1}    5.63{col 68}{space 3}0.000{col 76}{space 4} .3764783{col 89}{space 3} .7837718
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       329
{txt}{col 1}Number of PSUs{col 20}= {res}      135{txt}{col 49}Population size{col 67}={res} 328.876564
{txt}{col 49}Design df{col 67}= {res}       134
{txt}{col 49}F({res}  13{txt},{res}    122{txt}){col 67}= {res}      2.24
{txt}{col 49}Prob > F{col 67}= {res}    0.0117
{txt}{col 49}R-squared{col 67}= {res}    0.0856

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth25 {c |}{col 36}{res}{space 2} -.132829{col 48}{space 2} .0605755{col 59}{space 1}   -2.19{col 68}{space 3}0.030{col 76}{space 4}-.2526369{col 89}{space 3}-.0130212
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0325756{col 48}{space 2} .0983204{col 59}{space 1}    0.33{col 68}{space 3}0.741{col 76}{space 4}-.1618849{col 89}{space 3} .2270361
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth25#c.st_ethno {c |}{col 36}{res}{space 2} .4184931{col 48}{space 2} .1407473{col 59}{space 1}    2.97{col 68}{space 3}0.003{col 76}{space 4} .1401194{col 89}{space 3} .6968668
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0328344{col 48}{space 2} .0316877{col 59}{space 1}    1.04{col 68}{space 3}0.302{col 76}{space 4}-.0298384{col 89}{space 3} .0955072
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .345477{col 48}{space 2} .2763799{col 59}{space 1}    1.25{col 68}{space 3}0.213{col 76}{space 4}-.2011542{col 89}{space 3} .8921083
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2752204{col 48}{space 2} .3265993{col 59}{space 1}   -0.84{col 68}{space 3}0.401{col 76}{space 4} -.921177{col 89}{space 3} .3707362
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} -.089931{col 48}{space 2} .0687913{col 59}{space 1}   -1.31{col 68}{space 3}0.193{col 76}{space 4}-.2259883{col 89}{space 3} .0461262
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}  -.03627{col 48}{space 2}  .080771{col 59}{space 1}   -0.45{col 68}{space 3}0.654{col 76}{space 4}-.1960209{col 89}{space 3} .1234809
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0620789{col 48}{space 2} .0779184{col 59}{space 1}   -0.80{col 68}{space 3}0.427{col 76}{space 4}-.2161879{col 89}{space 3}   .09203
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0701048{col 48}{space 2} .0660515{col 59}{space 1}   -1.06{col 68}{space 3}0.290{col 76}{space 4}-.2007432{col 89}{space 3} .0605336
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1406604{col 48}{space 2} .0791761{col 59}{space 1}   -1.78{col 68}{space 3}0.078{col 76}{space 4}-.2972569{col 89}{space 3} .0159362
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0135777{col 48}{space 2} .0937795{col 59}{space 1}   -0.14{col 68}{space 3}0.885{col 76}{space 4}-.1990573{col 89}{space 3} .1719018
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1416223{col 48}{space 2} .1045079{col 59}{space 1}   -1.36{col 68}{space 3}0.178{col 76}{space 4}-.3483206{col 89}{space 3} .0650761
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .580125{col 48}{space 2}  .102965{col 59}{space 1}    5.63{col 68}{space 3}0.000{col 76}{space 4} .3764783{col 89}{space 3} .7837718
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       329
{txt}{col 1}Number of PSUs{col 20}= {res}      135{txt}{col 49}Population size{col 67}={res} 328.876564
{txt}{col 49}Design df{col 67}= {res}       134
{txt}{col 49}F({res}  13{txt},{res}    122{txt}){col 67}= {res}      2.24
{txt}{col 49}Prob > F{col 67}= {res}    0.0117
{txt}{col 49}R-squared{col 67}= {res}    0.0856

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth26 {c |}{col 36}{res}{space 2} -.132829{col 48}{space 2} .0605755{col 59}{space 1}   -2.19{col 68}{space 3}0.030{col 76}{space 4}-.2526369{col 89}{space 3}-.0130212
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0325756{col 48}{space 2} .0983204{col 59}{space 1}    0.33{col 68}{space 3}0.741{col 76}{space 4}-.1618849{col 89}{space 3} .2270361
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth26#c.st_ethno {c |}{col 36}{res}{space 2} .4184931{col 48}{space 2} .1407473{col 59}{space 1}    2.97{col 68}{space 3}0.003{col 76}{space 4} .1401194{col 89}{space 3} .6968668
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0328344{col 48}{space 2} .0316877{col 59}{space 1}    1.04{col 68}{space 3}0.302{col 76}{space 4}-.0298384{col 89}{space 3} .0955072
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .345477{col 48}{space 2} .2763799{col 59}{space 1}    1.25{col 68}{space 3}0.213{col 76}{space 4}-.2011542{col 89}{space 3} .8921083
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2752204{col 48}{space 2} .3265993{col 59}{space 1}   -0.84{col 68}{space 3}0.401{col 76}{space 4} -.921177{col 89}{space 3} .3707362
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} -.089931{col 48}{space 2} .0687913{col 59}{space 1}   -1.31{col 68}{space 3}0.193{col 76}{space 4}-.2259883{col 89}{space 3} .0461262
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}  -.03627{col 48}{space 2}  .080771{col 59}{space 1}   -0.45{col 68}{space 3}0.654{col 76}{space 4}-.1960209{col 89}{space 3} .1234809
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0620789{col 48}{space 2} .0779184{col 59}{space 1}   -0.80{col 68}{space 3}0.427{col 76}{space 4}-.2161879{col 89}{space 3}   .09203
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0701048{col 48}{space 2} .0660515{col 59}{space 1}   -1.06{col 68}{space 3}0.290{col 76}{space 4}-.2007432{col 89}{space 3} .0605336
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1406604{col 48}{space 2} .0791761{col 59}{space 1}   -1.78{col 68}{space 3}0.078{col 76}{space 4}-.2972569{col 89}{space 3} .0159362
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0135777{col 48}{space 2} .0937795{col 59}{space 1}   -0.14{col 68}{space 3}0.885{col 76}{space 4}-.1990573{col 89}{space 3} .1719018
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1416223{col 48}{space 2} .1045079{col 59}{space 1}   -1.36{col 68}{space 3}0.178{col 76}{space 4}-.3483206{col 89}{space 3} .0650761
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .580125{col 48}{space 2}  .102965{col 59}{space 1}    5.63{col 68}{space 3}0.000{col 76}{space 4} .3764783{col 89}{space 3} .7837718
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       334
{txt}{col 1}Number of PSUs{col 20}= {res}      135{txt}{col 49}Population size{col 67}={res} 334.345221
{txt}{col 49}Design df{col 67}= {res}       134
{txt}{col 49}F({res}  13{txt},{res}    122{txt}){col 67}= {res}      2.17
{txt}{col 49}Prob > F{col 67}= {res}    0.0145
{txt}{col 49}R-squared{col 67}= {res}    0.0825

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth27 {c |}{col 36}{res}{space 2} -.131362{col 48}{space 2} .0593127{col 59}{space 1}   -2.21{col 68}{space 3}0.028{col 76}{space 4}-.2486722{col 89}{space 3}-.0140518
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0347165{col 48}{space 2}  .098092{col 59}{space 1}    0.35{col 68}{space 3}0.724{col 76}{space 4}-.1592924{col 89}{space 3} .2287253
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth27#c.st_ethno {c |}{col 36}{res}{space 2} .4112478{col 48}{space 2} .1393957{col 59}{space 1}    2.95{col 68}{space 3}0.004{col 76}{space 4} .1355474{col 89}{space 3} .6869482
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0301946{col 48}{space 2} .0313846{col 59}{space 1}    0.96{col 68}{space 3}0.338{col 76}{space 4}-.0318786{col 89}{space 3} .0922678
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .3338056{col 48}{space 2} .2753152{col 59}{space 1}    1.21{col 68}{space 3}0.227{col 76}{space 4}-.2107198{col 89}{space 3}  .878331
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2676959{col 48}{space 2}  .326473{col 59}{space 1}   -0.82{col 68}{space 3}0.414{col 76}{space 4}-.9134026{col 89}{space 3} .3780107
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0907845{col 48}{space 2} .0692261{col 59}{space 1}   -1.31{col 68}{space 3}0.192{col 76}{space 4}-.2277016{col 89}{space 3} .0461327
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0352727{col 48}{space 2} .0810683{col 59}{space 1}   -0.44{col 68}{space 3}0.664{col 76}{space 4}-.1956118{col 89}{space 3} .1250663
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0690927{col 48}{space 2} .0774376{col 59}{space 1}   -0.89{col 68}{space 3}0.374{col 76}{space 4}-.2222507{col 89}{space 3} .0840654
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0608891{col 48}{space 2} .0605274{col 59}{space 1}   -1.01{col 68}{space 3}0.316{col 76}{space 4}-.1806018{col 89}{space 3} .0588236
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1282432{col 48}{space 2} .0738939{col 59}{space 1}   -1.74{col 68}{space 3}0.085{col 76}{space 4}-.2743924{col 89}{space 3}  .017906
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0016106{col 48}{space 2} .0902342{col 59}{space 1}   -0.02{col 68}{space 3}0.986{col 76}{space 4}-.1800781{col 89}{space 3}  .176857
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1216988{col 48}{space 2} .0998945{col 59}{space 1}   -1.22{col 68}{space 3}0.225{col 76}{space 4}-.3192727{col 89}{space 3}  .075875
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5750019{col 48}{space 2} .1021009{col 59}{space 1}    5.63{col 68}{space 3}0.000{col 76}{space 4} .3730642{col 89}{space 3} .7769396
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       338
{txt}{col 1}Number of PSUs{col 20}= {res}      135{txt}{col 49}Population size{col 67}={res} 339.280528
{txt}{col 49}Design df{col 67}= {res}       134
{txt}{col 49}F({res}  13{txt},{res}    122{txt}){col 67}= {res}      1.91
{txt}{col 49}Prob > F{col 67}= {res}    0.0353
{txt}{col 49}R-squared{col 67}= {res}    0.0713

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth28 {c |}{col 36}{res}{space 2} -.099049{col 48}{space 2}  .058565{col 59}{space 1}   -1.69{col 68}{space 3}0.093{col 76}{space 4}-.2148804{col 89}{space 3} .0167824
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0298878{col 48}{space 2} .0974956{col 59}{space 1}    0.31{col 68}{space 3}0.760{col 76}{space 4}-.1629415{col 89}{space 3} .2227172
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth28#c.st_ethno {c |}{col 36}{res}{space 2} .3498532{col 48}{space 2} .1409489{col 59}{space 1}    2.48{col 68}{space 3}0.014{col 76}{space 4} .0710808{col 89}{space 3} .6286255
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0227917{col 48}{space 2} .0320346{col 59}{space 1}    0.71{col 68}{space 3}0.478{col 76}{space 4}-.0405671{col 89}{space 3} .0861505
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .2723406{col 48}{space 2} .2787073{col 59}{space 1}    0.98{col 68}{space 3}0.330{col 76}{space 4}-.2788938{col 89}{space 3}  .823575
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.1819909{col 48}{space 2} .3279235{col 59}{space 1}   -0.55{col 68}{space 3}0.580{col 76}{space 4}-.8305665{col 89}{space 3} .4665847
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} -.095037{col 48}{space 2}  .069333{col 59}{space 1}   -1.37{col 68}{space 3}0.173{col 76}{space 4}-.2321656{col 89}{space 3} .0420916
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0266823{col 48}{space 2} .0822298{col 59}{space 1}   -0.32{col 68}{space 3}0.746{col 76}{space 4}-.1893186{col 89}{space 3}  .135954
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0780551{col 48}{space 2} .0780036{col 59}{space 1}   -1.00{col 68}{space 3}0.319{col 76}{space 4}-.2323326{col 89}{space 3} .0762225
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0603043{col 48}{space 2}  .061067{col 59}{space 1}   -0.99{col 68}{space 3}0.325{col 76}{space 4}-.1810841{col 89}{space 3} .0604756
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1269166{col 48}{space 2} .0751553{col 59}{space 1}   -1.69{col 68}{space 3}0.094{col 76}{space 4}-.2755607{col 89}{space 3} .0217276
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0034885{col 48}{space 2} .0902956{col 59}{space 1}    0.04{col 68}{space 3}0.969{col 76}{space 4}-.1751005{col 89}{space 3} .1820775
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1304038{col 48}{space 2} .0989117{col 59}{space 1}   -1.32{col 68}{space 3}0.190{col 76}{space 4}-.3260339{col 89}{space 3} .0652263
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5877081{col 48}{space 2} .1020935{col 59}{space 1}    5.76{col 68}{space 3}0.000{col 76}{space 4}  .385785{col 89}{space 3} .7896313
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       341
{txt}{col 1}Number of PSUs{col 20}= {res}      136{txt}{col 49}Population size{col 67}={res} 342.281531
{txt}{col 49}Design df{col 67}= {res}       135
{txt}{col 49}F({res}  13{txt},{res}    123{txt}){col 67}= {res}      2.00
{txt}{col 49}Prob > F{col 67}= {res}    0.0260
{txt}{col 49}R-squared{col 67}= {res}    0.0735

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth29 {c |}{col 36}{res}{space 2}-.0979167{col 48}{space 2} .0574784{col 59}{space 1}   -1.70{col 68}{space 3}0.091{col 76}{space 4}-.2115912{col 89}{space 3} .0157579
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0297935{col 48}{space 2} .0974687{col 59}{space 1}    0.31{col 68}{space 3}0.760{col 76}{space 4}-.1629696{col 89}{space 3} .2225565
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth29#c.st_ethno {c |}{col 36}{res}{space 2} .3528644{col 48}{space 2} .1382285{col 59}{space 1}    2.55{col 68}{space 3}0.012{col 76}{space 4}  .079491{col 89}{space 3} .6262377
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0241342{col 48}{space 2} .0318439{col 59}{space 1}    0.76{col 68}{space 3}0.450{col 76}{space 4}-.0388432{col 89}{space 3} .0871117
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .2801731{col 48}{space 2} .2783945{col 59}{space 1}    1.01{col 68}{space 3}0.316{col 76}{space 4}-.2704055{col 89}{space 3} .8307517
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.1862515{col 48}{space 2} .3279231{col 59}{space 1}   -0.57{col 68}{space 3}0.571{col 76}{space 4}-.8347825{col 89}{space 3} .4622795
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0952879{col 48}{space 2} .0691375{col 59}{space 1}   -1.38{col 68}{space 3}0.170{col 76}{space 4}-.2320207{col 89}{space 3} .0414449
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} -.024336{col 48}{space 2}  .081882{col 59}{space 1}   -0.30{col 68}{space 3}0.767{col 76}{space 4}-.1862735{col 89}{space 3} .1376014
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}  -.07611{col 48}{space 2} .0777175{col 59}{space 1}   -0.98{col 68}{space 3}0.329{col 76}{space 4}-.2298112{col 89}{space 3} .0775913
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0598618{col 48}{space 2} .0613611{col 59}{space 1}   -0.98{col 68}{space 3}0.331{col 76}{space 4}-.1812151{col 89}{space 3} .0614916
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1274733{col 48}{space 2} .0753464{col 59}{space 1}   -1.69{col 68}{space 3}0.093{col 76}{space 4}-.2764852{col 89}{space 3} .0215387
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0024856{col 48}{space 2}  .090416{col 59}{space 1}    0.03{col 68}{space 3}0.978{col 76}{space 4}-.1763294{col 89}{space 3} .1813007
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} -.129541{col 48}{space 2} .0990517{col 59}{space 1}   -1.31{col 68}{space 3}0.193{col 76}{space 4}-.3254348{col 89}{space 3} .0663528
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5838901{col 48}{space 2} .1020972{col 59}{space 1}    5.72{col 68}{space 3}0.000{col 76}{space 4} .3819733{col 89}{space 3} .7858069
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       344
{txt}{col 1}Number of PSUs{col 20}= {res}      137{txt}{col 49}Population size{col 67}={res} 345.282535
{txt}{col 49}Design df{col 67}= {res}       136
{txt}{col 49}F({res}  13{txt},{res}    124{txt}){col 67}= {res}      1.98
{txt}{col 49}Prob > F{col 67}= {res}    0.0276
{txt}{col 49}R-squared{col 67}= {res}    0.0739

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}ctreatmentwidth30 {c |}{col 36}{res}{space 2}-.0957463{col 48}{space 2} .0574839{col 59}{space 1}   -1.67{col 68}{space 3}0.098{col 76}{space 4}-.2094243{col 89}{space 3} .0179316
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0305663{col 48}{space 2} .0974824{col 59}{space 1}    0.31{col 68}{space 3}0.754{col 76}{space 4}-.1622111{col 89}{space 3} .2233438
{txt}{space 34} {c |}
{space 4}c.ctreatmentwidth30#c.st_ethno {c |}{col 36}{res}{space 2} .3526831{col 48}{space 2} .1380143{col 59}{space 1}    2.56{col 68}{space 3}0.012{col 76}{space 4} .0797514{col 89}{space 3} .6256148
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0243178{col 48}{space 2} .0315364{col 59}{space 1}    0.77{col 68}{space 3}0.442{col 76}{space 4}-.0380472{col 89}{space 3} .0866829
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .2797575{col 48}{space 2} .2734561{col 59}{space 1}    1.02{col 68}{space 3}0.308{col 76}{space 4}-.2610187{col 89}{space 3} .8205336
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.1869069{col 48}{space 2} .3233892{col 59}{space 1}   -0.58{col 68}{space 3}0.564{col 76}{space 4}-.8264287{col 89}{space 3} .4526149
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0944917{col 48}{space 2} .0691403{col 59}{space 1}   -1.37{col 68}{space 3}0.174{col 76}{space 4}-.2312209{col 89}{space 3} .0422375
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} -.024927{col 48}{space 2}  .081815{col 59}{space 1}   -0.30{col 68}{space 3}0.761{col 76}{space 4}-.1867211{col 89}{space 3} .1368671
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0748918{col 48}{space 2} .0776324{col 59}{space 1}   -0.96{col 68}{space 3}0.336{col 76}{space 4}-.2284146{col 89}{space 3}  .078631
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0581907{col 48}{space 2} .0620074{col 59}{space 1}   -0.94{col 68}{space 3}0.350{col 76}{space 4}-.1808141{col 89}{space 3} .0644326
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} -.127143{col 48}{space 2} .0759671{col 59}{space 1}   -1.67{col 68}{space 3}0.096{col 76}{space 4}-.2773725{col 89}{space 3} .0230866
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}  .002502{col 48}{space 2} .0905263{col 59}{space 1}    0.03{col 68}{space 3}0.978{col 76}{space 4}-.1765194{col 89}{space 3} .1815233
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1292915{col 48}{space 2} .0981446{col 59}{space 1}   -1.32{col 68}{space 3}0.190{col 76}{space 4}-.3233784{col 89}{space 3} .0647954
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5824004{col 48}{space 2} .1016254{col 59}{space 1}    5.73{col 68}{space 3}0.000{col 76}{space 4} .3814299{col 89}{space 3} .7833709
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. tab ctreatmentwidth7 if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. 

{txt}ctreatmentw {c |}
      idth7 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        202       79.53       79.53
{txt}          1 {c |}{res}         52       20.47      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        254      100.00
{txt}
{com}. tab ctreatmentwidth16 if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. 

{txt}ctreatmentw {c |}
     idth16 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        202       68.01       68.01
{txt}          1 {c |}{res}         95       31.99      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        297      100.00
{txt}
{com}. tab ctreatmentwidth30 if st_terrori!=. & st_ethno!=. & sex!=. & st_age!=. & proedu2!=. & work2!=. 

{txt}ctreatmentw {c |}
     idth30 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        202       58.72       58.72
{txt}          1 {c |}{res}        142       41.28      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        344      100.00
{txt}
{com}. di 142/52
{res}2.7307692
{txt}
{com}. *=2.73
. *-------------------------------------------------------------------------------
. 
. 
. 
{txt}end of do-file

{com}. do "Appendix_R.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. keep if german==1 & governmentsplit!=.                                  
{txt}(1,898 observations deleted)

{com}. 
. *-------------------------------------------------------------------------------
. *Appendix R: Additional robustness check: The activation effect using 
. *alternative control periods
. *-------------------------------------------------------------------------------
. 
. 
. 
.                                                                 *Table R1*
. 
. *Full ethnocentrism index and civil liberties with reference to terrorism
. *Control group 12/04/2016-02/05/2016
. svy: regress st_terrori c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if control_1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       508
{txt}{col 1}Number of PSUs{col 20}= {res}      145{txt}{col 49}Population size{col 67}={res} 481.785308
{txt}{col 49}Design df{col 67}= {res}       144
{txt}{col 49}F({res}  11{txt},{res}    134{txt}){col 67}= {res}      4.07
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0939

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2991092{col 48}{space 2} .0729497{col 59}{space 1}    4.10{col 68}{space 3}0.000{col 76}{space 4} .1549186{col 89}{space 3} .4432998
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0427734{col 48}{space 2} .0248341{col 59}{space 1}    1.72{col 68}{space 3}0.087{col 76}{space 4} -.006313{col 89}{space 3} .0918598
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}-.0621328{col 48}{space 2} .2516174{col 59}{space 1}   -0.25{col 68}{space 3}0.805{col 76}{space 4}-.5594734{col 89}{space 3} .4352078
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} .2207882{col 48}{space 2} .3010858{col 59}{space 1}    0.73{col 68}{space 3}0.465{col 76}{space 4}-.3743305{col 89}{space 3}  .815907
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0620402{col 48}{space 2} .0607971{col 59}{space 1}   -1.02{col 68}{space 3}0.309{col 76}{space 4}-.1822103{col 89}{space 3} .0581299
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0916563{col 48}{space 2} .0597884{col 59}{space 1}   -1.53{col 68}{space 3}0.127{col 76}{space 4}-.2098325{col 89}{space 3} .0265199
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1194382{col 48}{space 2} .0652139{col 59}{space 1}   -1.83{col 68}{space 3}0.069{col 76}{space 4}-.2483383{col 89}{space 3} .0094619
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0746523{col 48}{space 2} .0568955{col 59}{space 1}    1.31{col 68}{space 3}0.192{col 76}{space 4}-.0378058{col 89}{space 3} .1871105
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0521928{col 48}{space 2} .0745646{col 59}{space 1}    0.70{col 68}{space 3}0.485{col 76}{space 4}-.0951898{col 89}{space 3} .1995754
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0049616{col 48}{space 2} .0762594{col 59}{space 1}   -0.07{col 68}{space 3}0.948{col 76}{space 4}-.1556941{col 89}{space 3} .1457708
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .1136417{col 48}{space 2} .0892201{col 59}{space 1}    1.27{col 68}{space 3}0.205{col 76}{space 4}-.0627085{col 89}{space 3}  .289992
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .3989838{col 48}{space 2} .1078445{col 59}{space 1}    3.70{col 68}{space 3}0.000{col 76}{space 4} .1858209{col 89}{space 3} .6121466
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_ethno i.control_1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       508
{txt}{col 1}Number of PSUs{col 20}= {res}      145{txt}{col 49}Population size{col 67}={res} 481.785308
{txt}{col 49}Design df{col 67}= {res}       144
{txt}{col 49}F({res}  12{txt},{res}    133{txt}){col 67}= {res}      4.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1064

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2965607{col 48}{space 2} .0712588{col 59}{space 1}    4.16{col 68}{space 3}0.000{col 76}{space 4} .1557123{col 89}{space 3} .4374091
{txt}{space 23}1.control_1 {c |}{col 36}{res}{space 2} .1005175{col 48}{space 2} .0355745{col 59}{space 1}    2.83{col 68}{space 3}0.005{col 76}{space 4} .0302019{col 89}{space 3} .1708332
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0402526{col 48}{space 2} .0247002{col 59}{space 1}    1.63{col 68}{space 3}0.105{col 76}{space 4}-.0085692{col 89}{space 3} .0890744
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}-.0389951{col 48}{space 2} .2467896{col 59}{space 1}   -0.16{col 68}{space 3}0.875{col 76}{space 4}-.5267934{col 89}{space 3} .4488031
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} .1770559{col 48}{space 2} .2966067{col 59}{space 1}    0.60{col 68}{space 3}0.551{col 76}{space 4}-.4092095{col 89}{space 3} .7633213
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0499612{col 48}{space 2} .0580955{col 59}{space 1}   -0.86{col 68}{space 3}0.391{col 76}{space 4}-.1647913{col 89}{space 3}  .064869
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0792561{col 48}{space 2} .0572597{col 59}{space 1}   -1.38{col 68}{space 3}0.168{col 76}{space 4}-.1924342{col 89}{space 3}  .033922
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1145238{col 48}{space 2} .0623928{col 59}{space 1}   -1.84{col 68}{space 3}0.068{col 76}{space 4}-.2378478{col 89}{space 3} .0088003
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}  .067688{col 48}{space 2} .0574795{col 59}{space 1}    1.18{col 68}{space 3}0.241{col 76}{space 4}-.0459245{col 89}{space 3} .1813006
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0534679{col 48}{space 2} .0752993{col 59}{space 1}    0.71{col 68}{space 3}0.479{col 76}{space 4}-.0953668{col 89}{space 3} .2023025
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0014524{col 48}{space 2} .0773695{col 59}{space 1}    0.02{col 68}{space 3}0.985{col 76}{space 4}-.1514743{col 89}{space 3} .1543791
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .1035276{col 48}{space 2} .0900298{col 59}{space 1}    1.15{col 68}{space 3}0.252{col 76}{space 4}-.0744231{col 89}{space 3} .2814783
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .3853879{col 48}{space 2} .1064938{col 59}{space 1}    3.62{col 68}{space 3}0.000{col 76}{space 4} .1748949{col 89}{space 3} .5958808
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_ethno##i.control_1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       508
{txt}{col 1}Number of PSUs{col 20}= {res}      145{txt}{col 49}Population size{col 67}={res} 481.785308
{txt}{col 49}Design df{col 67}= {res}       144
{txt}{col 49}F({res}  13{txt},{res}    132{txt}){col 67}= {res}      5.48
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1115

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2617243{col 48}{space 2} .0755919{col 59}{space 1}    3.46{col 68}{space 3}0.001{col 76}{space 4} .1123112{col 89}{space 3} .4111373
{txt}{space 23}1.control_1 {c |}{col 36}{res}{space 2}-.0220958{col 48}{space 2} .0584466{col 59}{space 1}   -0.38{col 68}{space 3}0.706{col 76}{space 4}-.1376199{col 89}{space 3} .0934283
{txt}{space 34} {c |}
{space 14}control_1#c.st_ethno {c |}
{space 32}1  {c |}{col 36}{res}{space 2}  .313409{col 48}{space 2}  .127419{col 59}{space 1}    2.46{col 68}{space 3}0.015{col 76}{space 4} .0615558{col 89}{space 3} .5652622
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0414043{col 48}{space 2} .0245717{col 59}{space 1}    1.69{col 68}{space 3}0.094{col 76}{space 4}-.0071635{col 89}{space 3} .0899722
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}-.0146175{col 48}{space 2} .2476115{col 59}{space 1}   -0.06{col 68}{space 3}0.953{col 76}{space 4}-.5040402{col 89}{space 3} .4748052
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} .1370044{col 48}{space 2} .2985146{col 59}{space 1}    0.46{col 68}{space 3}0.647{col 76}{space 4}-.4530321{col 89}{space 3} .7270409
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0389031{col 48}{space 2} .0566412{col 59}{space 1}   -0.69{col 68}{space 3}0.493{col 76}{space 4}-.1508587{col 89}{space 3} .0730524
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0666808{col 48}{space 2}  .055951{col 59}{space 1}   -1.19{col 68}{space 3}0.235{col 76}{space 4}-.1772721{col 89}{space 3} .0439105
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1053713{col 48}{space 2} .0607523{col 59}{space 1}   -1.73{col 68}{space 3}0.085{col 76}{space 4}-.2254529{col 89}{space 3} .0147102
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0624269{col 48}{space 2} .0560247{col 59}{space 1}    1.11{col 68}{space 3}0.267{col 76}{space 4}-.0483102{col 89}{space 3}  .173164
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0496747{col 48}{space 2} .0738829{col 59}{space 1}    0.67{col 68}{space 3}0.502{col 76}{space 4}-.0963603{col 89}{space 3} .1957098
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0037418{col 48}{space 2} .0755579{col 59}{space 1}   -0.05{col 68}{space 3}0.961{col 76}{space 4}-.1530876{col 89}{space 3} .1456039
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0991012{col 48}{space 2} .0899011{col 59}{space 1}    1.10{col 68}{space 3}0.272{col 76}{space 4}-.0785952{col 89}{space 3} .2767975
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .3917798{col 48}{space 2} .1054814{col 59}{space 1}    3.71{col 68}{space 3}0.000{col 76}{space 4}  .183288{col 89}{space 3} .6002716
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. 
. *Control group 03/05/2016-22/05/2016                            
. svy: regress st_terrori c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if control_2!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       292
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 285.326403
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  11{txt},{res}    106{txt}){col 67}= {res}      3.72
{txt}{col 49}Prob > F{col 67}= {res}    0.0002
{txt}{col 49}R-squared{col 67}= {res}    0.1244

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .3463252{col 48}{space 2} .0948652{col 59}{space 1}    3.65{col 68}{space 3}0.000{col 76}{space 4} .1584328{col 89}{space 3} .5342177
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0206727{col 48}{space 2} .0274438{col 59}{space 1}   -0.75{col 68}{space 3}0.453{col 76}{space 4}-.0750285{col 89}{space 3} .0336831
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}-.6547383{col 48}{space 2} .2829565{col 59}{space 1}   -2.31{col 68}{space 3}0.022{col 76}{space 4}-1.215169{col 89}{space 3}-.0943073
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} .6076176{col 48}{space 2} .3341675{col 59}{space 1}    1.82{col 68}{space 3}0.072{col 76}{space 4}-.0542432{col 89}{space 3} 1.269478
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} .0970473{col 48}{space 2} .0691796{col 59}{space 1}    1.40{col 68}{space 3}0.163{col 76}{space 4}-.0399717{col 89}{space 3} .2340663
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0657119{col 48}{space 2} .0680555{col 59}{space 1}    0.97{col 68}{space 3}0.336{col 76}{space 4}-.0690806{col 89}{space 3} .2005043
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} .0798027{col 48}{space 2} .0681668{col 59}{space 1}    1.17{col 68}{space 3}0.244{col 76}{space 4}-.0552102{col 89}{space 3} .2148156
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .1230561{col 48}{space 2} .0706259{col 59}{space 1}    1.74{col 68}{space 3}0.084{col 76}{space 4}-.0168275{col 89}{space 3} .2629397
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0329242{col 48}{space 2} .0776311{col 59}{space 1}    0.42{col 68}{space 3}0.672{col 76}{space 4} -.120834{col 89}{space 3} .1866824
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1551014{col 48}{space 2} .0929442{col 59}{space 1}    1.67{col 68}{space 3}0.098{col 76}{space 4}-.0289863{col 89}{space 3} .3391892
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0032136{col 48}{space 2} .1103362{col 59}{space 1}   -0.03{col 68}{space 3}0.977{col 76}{space 4}-.2217483{col 89}{space 3} .2153212
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .4178605{col 48}{space 2} .1065793{col 59}{space 1}    3.92{col 68}{space 3}0.000{col 76}{space 4} .2067668{col 89}{space 3} .6289541
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_ethno i.control_2 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       292
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 285.326403
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  12{txt},{res}    105{txt}){col 67}= {res}      3.57
{txt}{col 49}Prob > F{col 67}= {res}    0.0002
{txt}{col 49}R-squared{col 67}= {res}    0.1415

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .3390441{col 48}{space 2} .0921698{col 59}{space 1}    3.68{col 68}{space 3}0.000{col 76}{space 4} .1564902{col 89}{space 3}  .521598
{txt}{space 23}1.control_2 {c |}{col 36}{res}{space 2} .0892843{col 48}{space 2} .0440854{col 59}{space 1}    2.03{col 68}{space 3}0.045{col 76}{space 4} .0019675{col 89}{space 3} .1766011
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0232988{col 48}{space 2} .0272416{col 59}{space 1}   -0.86{col 68}{space 3}0.394{col 76}{space 4}-.0772542{col 89}{space 3} .0306566
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}-.6298993{col 48}{space 2} .2783941{col 59}{space 1}   -2.26{col 68}{space 3}0.026{col 76}{space 4}-1.181294{col 89}{space 3}-.0785048
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} .5500334{col 48}{space 2}  .331568{col 59}{space 1}    1.66{col 68}{space 3}0.100{col 76}{space 4}-.1066789{col 89}{space 3} 1.206746
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} .0950704{col 48}{space 2} .0670208{col 59}{space 1}    1.42{col 68}{space 3}0.159{col 76}{space 4}-.0376727{col 89}{space 3} .2278134
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0669836{col 48}{space 2} .0657831{col 59}{space 1}    1.02{col 68}{space 3}0.311{col 76}{space 4}-.0633082{col 89}{space 3} .1972753
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} .0678659{col 48}{space 2} .0662342{col 59}{space 1}    1.02{col 68}{space 3}0.308{col 76}{space 4}-.0633192{col 89}{space 3} .1990511
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .1157179{col 48}{space 2} .0726153{col 59}{space 1}    1.59{col 68}{space 3}0.114{col 76}{space 4}-.0281059{col 89}{space 3} .2595416
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0370834{col 48}{space 2} .0784419{col 59}{space 1}    0.47{col 68}{space 3}0.637{col 76}{space 4}-.1182806{col 89}{space 3} .1924475
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1644592{col 48}{space 2} .0941198{col 59}{space 1}    1.75{col 68}{space 3}0.083{col 76}{space 4}-.0219569{col 89}{space 3} .3508753
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0146897{col 48}{space 2} .1142884{col 59}{space 1}   -0.13{col 68}{space 3}0.898{col 76}{space 4}-.2410522{col 89}{space 3} .2116728
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .4156706{col 48}{space 2} .1067703{col 59}{space 1}    3.89{col 68}{space 3}0.000{col 76}{space 4} .2041986{col 89}{space 3} .6271426
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_ethno##i.control_2 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       292
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 285.326403
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      4.59
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1651

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2507096{col 48}{space 2}  .106433{col 59}{space 1}    2.36{col 68}{space 3}0.020{col 76}{space 4} .0399056{col 89}{space 3} .4615135
{txt}{space 23}1.control_2 {c |}{col 36}{res}{space 2}-.1081812{col 48}{space 2} .0710443{col 59}{space 1}   -1.52{col 68}{space 3}0.131{col 76}{space 4}-.2488934{col 89}{space 3}  .032531
{txt}{space 34} {c |}
{space 14}control_2#c.st_ethno {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .5064353{col 48}{space 2} .1717991{col 59}{space 1}    2.95{col 68}{space 3}0.004{col 76}{space 4} .1661655{col 89}{space 3} .8467051
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0221596{col 48}{space 2} .0269077{col 59}{space 1}   -0.82{col 68}{space 3}0.412{col 76}{space 4}-.0754537{col 89}{space 3} .0311344
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}-.5532563{col 48}{space 2} .2735828{col 59}{space 1}   -2.02{col 68}{space 3}0.045{col 76}{space 4}-1.095122{col 89}{space 3} -.011391
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} .4395954{col 48}{space 2} .3295986{col 59}{space 1}    1.33{col 68}{space 3}0.185{col 76}{space 4}-.2132162{col 89}{space 3} 1.092407
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} .0936633{col 48}{space 2} .0666975{col 59}{space 1}    1.40{col 68}{space 3}0.163{col 76}{space 4}-.0384394{col 89}{space 3} .2257661
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0676947{col 48}{space 2} .0664601{col 59}{space 1}    1.02{col 68}{space 3}0.311{col 76}{space 4}-.0639379{col 89}{space 3} .1993273
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} .0664845{col 48}{space 2}  .066875{col 59}{space 1}    0.99{col 68}{space 3}0.322{col 76}{space 4}-.0659698{col 89}{space 3} .1989389
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .1073825{col 48}{space 2} .0685704{col 59}{space 1}    1.57{col 68}{space 3}0.120{col 76}{space 4}-.0284298{col 89}{space 3} .2431949
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0301684{col 48}{space 2} .0747494{col 59}{space 1}    0.40{col 68}{space 3}0.687{col 76}{space 4}-.1178822{col 89}{space 3} .1782189
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1531214{col 48}{space 2} .0923762{col 59}{space 1}    1.66{col 68}{space 3}0.100{col 76}{space 4}-.0298413{col 89}{space 3} .3360841
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0162595{col 48}{space 2} .1110374{col 59}{space 1}   -0.15{col 68}{space 3}0.884{col 76}{space 4} -.236183{col 89}{space 3}  .203664
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .4496433{col 48}{space 2} .1051321{col 59}{space 1}    4.28{col 68}{space 3}0.000{col 76}{space 4} .2414159{col 89}{space 3} .6578707
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. 
. *Control group 23/05/2016-11/06/2016
. svy: regress st_terrori c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if control_3!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       161
{txt}{col 1}Number of PSUs{col 20}= {res}       84{txt}{col 49}Population size{col 67}={res} 163.622272
{txt}{col 49}Design df{col 67}= {res}        83
{txt}{col 49}F({res}  11{txt},{res}     73{txt}){col 67}= {res}      7.87
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2710

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .5449367{col 48}{space 2} .1070019{col 59}{space 1}    5.09{col 68}{space 3}0.000{col 76}{space 4} .3321141{col 89}{space 3} .7577593
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0571987{col 48}{space 2} .0445372{col 59}{space 1}    1.28{col 68}{space 3}0.203{col 76}{space 4} -.031384{col 89}{space 3} .1457815
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .1477414{col 48}{space 2}  .389813{col 59}{space 1}    0.38{col 68}{space 3}0.706{col 76}{space 4}-.6275809{col 89}{space 3} .9230637
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6515716{col 48}{space 2} .5127433{col 59}{space 1}   -1.27{col 68}{space 3}0.207{col 76}{space 4}-1.671397{col 89}{space 3} .3682542
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.2543433{col 48}{space 2} .0896985{col 59}{space 1}   -2.84{col 68}{space 3}0.006{col 76}{space 4}  -.43275{col 89}{space 3}-.0759365
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1782084{col 48}{space 2} .0919178{col 59}{space 1}   -1.94{col 68}{space 3}0.056{col 76}{space 4}-.3610292{col 89}{space 3} .0046124
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} -.242891{col 48}{space 2} .0973904{col 59}{space 1}   -2.49{col 68}{space 3}0.015{col 76}{space 4}-.4365966{col 89}{space 3}-.0491853
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0588797{col 48}{space 2}  .160768{col 59}{space 1}   -0.37{col 68}{space 3}0.715{col 76}{space 4}-.3786407{col 89}{space 3} .2608813
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .1001369{col 48}{space 2} .1748797{col 59}{space 1}    0.57{col 68}{space 3}0.568{col 76}{space 4}-.2476918{col 89}{space 3} .4479655
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0430981{col 48}{space 2} .1717524{col 59}{space 1}   -0.25{col 68}{space 3}0.802{col 76}{space 4}-.3847067{col 89}{space 3} .2985105
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.2051164{col 48}{space 2} .1674232{col 59}{space 1}   -1.23{col 68}{space 3}0.224{col 76}{space 4}-.5381143{col 89}{space 3} .1278816
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6575714{col 48}{space 2}   .18755{col 59}{space 1}    3.51{col 68}{space 3}0.001{col 76}{space 4} .2845419{col 89}{space 3} 1.030601
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_ethno i.control_3 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       161
{txt}{col 1}Number of PSUs{col 20}= {res}       84{txt}{col 49}Population size{col 67}={res} 163.622272
{txt}{col 49}Design df{col 67}= {res}        83
{txt}{col 49}F({res}  12{txt},{res}     72{txt}){col 67}= {res}     10.10
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.3053

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .5038217{col 48}{space 2} .0985186{col 59}{space 1}    5.11{col 68}{space 3}0.000{col 76}{space 4} .3078723{col 89}{space 3} .6997712
{txt}{space 23}1.control_3 {c |}{col 36}{res}{space 2} .1129851{col 48}{space 2} .0413559{col 59}{space 1}    2.73{col 68}{space 3}0.008{col 76}{space 4} .0307298{col 89}{space 3} .1952403
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0516272{col 48}{space 2} .0451909{col 59}{space 1}    1.14{col 68}{space 3}0.257{col 76}{space 4}-.0382557{col 89}{space 3} .1415102
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .0763011{col 48}{space 2} .3591823{col 59}{space 1}    0.21{col 68}{space 3}0.832{col 76}{space 4} -.638098{col 89}{space 3} .7907002
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6121512{col 48}{space 2} .4603418{col 59}{space 1}   -1.33{col 68}{space 3}0.187{col 76}{space 4}-1.527753{col 89}{space 3} .3034501
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.2338335{col 48}{space 2} .0901961{col 59}{space 1}   -2.59{col 68}{space 3}0.011{col 76}{space 4}  -.41323{col 89}{space 3}-.0544371
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1612008{col 48}{space 2} .0916359{col 59}{space 1}   -1.76{col 68}{space 3}0.082{col 76}{space 4} -.343461{col 89}{space 3} .0210593
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} -.254567{col 48}{space 2} .0931968{col 59}{space 1}   -2.73{col 68}{space 3}0.008{col 76}{space 4}-.4399317{col 89}{space 3}-.0692023
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0207945{col 48}{space 2} .1779904{col 59}{space 1}   -0.12{col 68}{space 3}0.907{col 76}{space 4}-.3748103{col 89}{space 3} .3332214
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}  .145869{col 48}{space 2} .1910118{col 59}{space 1}    0.76{col 68}{space 3}0.447{col 76}{space 4}-.2340458{col 89}{space 3} .5257837
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0222549{col 48}{space 2} .1898463{col 59}{space 1}    0.12{col 68}{space 3}0.907{col 76}{space 4}-.3553417{col 89}{space 3} .3998515
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1869602{col 48}{space 2} .1849561{col 59}{space 1}   -1.01{col 68}{space 3}0.315{col 76}{space 4}-.5548305{col 89}{space 3} .1809102
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6109348{col 48}{space 2} .2075014{col 59}{space 1}    2.94{col 68}{space 3}0.004{col 76}{space 4} .1982228{col 89}{space 3} 1.023647
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori c.st_ethno##i.control_3 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       161
{txt}{col 1}Number of PSUs{col 20}= {res}       84{txt}{col 49}Population size{col 67}={res} 163.622272
{txt}{col 49}Design df{col 67}= {res}        83
{txt}{col 49}F({res}  13{txt},{res}     71{txt}){col 67}= {res}     10.03
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.3122

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .4144023{col 48}{space 2} .1217666{col 59}{space 1}    3.40{col 68}{space 3}0.001{col 76}{space 4} .1722135{col 89}{space 3} .6565911
{txt}{space 23}1.control_3 {c |}{col 36}{res}{space 2} .0199309{col 48}{space 2} .0655576{col 59}{space 1}    0.30{col 68}{space 3}0.762{col 76}{space 4}-.1104606{col 89}{space 3} .1503224
{txt}{space 34} {c |}
{space 14}control_3#c.st_ethno {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .2481069{col 48}{space 2} .1546356{col 59}{space 1}    1.60{col 68}{space 3}0.112{col 76}{space 4} -.059457{col 89}{space 3} .5556709
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}  .055578{col 48}{space 2} .0453334{col 59}{space 1}    1.23{col 68}{space 3}0.224{col 76}{space 4}-.0345882{col 89}{space 3} .1457443
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .0991584{col 48}{space 2} .3538658{col 59}{space 1}    0.28{col 68}{space 3}0.780{col 76}{space 4}-.6046665{col 89}{space 3} .8029832
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6593105{col 48}{space 2} .4552901{col 59}{space 1}   -1.45{col 68}{space 3}0.151{col 76}{space 4}-1.564864{col 89}{space 3} .2462433
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.2178471{col 48}{space 2}  .093795{col 59}{space 1}   -2.32{col 68}{space 3}0.023{col 76}{space 4}-.4044016{col 89}{space 3}-.0312927
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1460788{col 48}{space 2} .0952026{col 59}{space 1}   -1.53{col 68}{space 3}0.129{col 76}{space 4}-.3354329{col 89}{space 3} .0432753
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} -.242277{col 48}{space 2}  .094358{col 59}{space 1}   -2.57{col 68}{space 3}0.012{col 76}{space 4}-.4299514{col 89}{space 3}-.0546027
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0322243{col 48}{space 2} .1592046{col 59}{space 1}   -0.20{col 68}{space 3}0.840{col 76}{space 4} -.348876{col 89}{space 3} .2844273
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .1416285{col 48}{space 2} .1736817{col 59}{space 1}    0.82{col 68}{space 3}0.417{col 76}{space 4}-.2038173{col 89}{space 3} .4870744
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0094399{col 48}{space 2} .1727038{col 59}{space 1}    0.05{col 68}{space 3}0.957{col 76}{space 4} -.334061{col 89}{space 3} .3529408
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.2044494{col 48}{space 2} .1701305{col 59}{space 1}   -1.20{col 68}{space 3}0.233{col 76}{space 4} -.542832{col 89}{space 3} .1339333
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6372925{col 48}{space 2} .1916124{col 59}{space 1}    3.33{col 68}{space 3}0.001{col 76}{space 4} .2561831{col 89}{space 3} 1.018402
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. ********************************************************************************
. 
.                                 *NOT SHOWN IN ONLINE APPENDIX*
. 
. *Marginsplot for main studied groups (standard) and alternative control groups
. 
. *Marginsplot for normal control
. svy: regress st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      4.88
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1330

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0007221{col 48}{space 2} .0977606{col 59}{space 1}    0.01{col 68}{space 3}0.994{col 76}{space 4}-.1929051{col 89}{space 3} .1943494
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1518117{col 48}{space 2}  .070707{col 59}{space 1}   -2.15{col 68}{space 3}0.034{col 76}{space 4}-.2918558{col 89}{space 3}-.0117676
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6750141{col 48}{space 2} .1507665{col 59}{space 1}    4.48{col 68}{space 3}0.000{col 76}{space 4}  .376402{col 89}{space 3} .9736263
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0061306{col 48}{space 2} .0367219{col 59}{space 1}   -0.17{col 68}{space 3}0.868{col 76}{space 4}-.0788629{col 89}{space 3} .0666017
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .7003869{col 48}{space 2} .3186851{col 59}{space 1}    2.20{col 68}{space 3}0.030{col 76}{space 4} .0691909{col 89}{space 3} 1.331583
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6554761{col 48}{space 2} .3602407{col 59}{space 1}   -1.82{col 68}{space 3}0.071{col 76}{space 4}-1.368978{col 89}{space 3} .0580259
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0975267{col 48}{space 2} .0645064{col 59}{space 1}   -1.51{col 68}{space 3}0.133{col 76}{space 4}-.2252897{col 89}{space 3} .0302363
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}    -.022{col 48}{space 2} .0792046{col 59}{space 1}   -0.28{col 68}{space 3}0.782{col 76}{space 4}-.1788748{col 89}{space 3} .1348747
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1095112{col 48}{space 2} .0748847{col 59}{space 1}   -1.46{col 68}{space 3}0.146{col 76}{space 4}-.2578299{col 89}{space 3} .0388074
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0841434{col 48}{space 2} .0728254{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2283832{col 89}{space 3} .0600964
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.1028858{col 48}{space 2} .0890782{col 59}{space 1}   -1.16{col 68}{space 3}0.250{col 76}{space 4}-.2793165{col 89}{space 3} .0735448
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0070997{col 48}{space 2} .1194672{col 59}{space 1}   -0.06{col 68}{space 3}0.953{col 76}{space 4}-.2437196{col 89}{space 3} .2295203
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.1023694{col 48}{space 2} .1167093{col 59}{space 1}   -0.88{col 68}{space 3}0.382{col 76}{space 4}-.3335268{col 89}{space 3}  .128788
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5497256{col 48}{space 2}    .1046{col 59}{space 1}    5.26{col 68}{space 3}0.000{col 76}{space 4} .3425521{col 89}{space 3} .7568991
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, at( treatment1=(0,1) st_ethno=(.375) work2=(2) (mean) sex st_age st_age2 proedu2) vce(unconditional)
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}       254

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.375}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.sex}{space 11}{txt:=} {space 3}.5422986 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.sex}{space 11}{txt:=} {space 3}.4577014 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age}{space 10}{txt:=} {space 3}.4490335 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age2}{space 9}{txt:=} {space 3}.2557779 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.proedu2}{space 7}{txt:=} {space 3}.0794278 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.proedu2}{space 7}{txt:=} {space 3}.4640748 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.proedu2}{space 7}{txt:=} {space 3}.1990935 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.proedu2}{space 7}{txt:=} {space 3}.2574038 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:work2}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.375}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:treatment1}{space 6}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.sex}{space 11}{txt:=} {space 3}.5422986 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.sex}{space 11}{txt:=} {space 3}.4577014 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age}{space 10}{txt:=} {space 3}.4490335 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age2}{space 9}{txt:=} {space 3}.2557779 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.proedu2}{space 7}{txt:=} {space 3}.0794278 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.proedu2}{space 7}{txt:=} {space 3}.4640748 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.proedu2}{space 7}{txt:=} {space 3}.1990935 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.proedu2}{space 7}{txt:=} {space 3}.2574038 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:work2}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5320596{col 26}{space 2} .0283123{col 37}{space 1}   18.79{col 46}{space 3}0.000{col 54}{space 4} .4759836{col 67}{space 3} .5881356
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6333782{col 26}{space 2}   .03956{col 37}{space 1}   16.01{col 46}{space 3}0.000{col 54}{space 4} .5550246{col 67}{space 3} .7117318
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, xdimension(treatment1) name(graph_t1, replace) recast(scatter) ytitle("") ylabel(0.4(0.05)0.8, labsize(medium) angle(horizontal) grid) xtitle("") xlabel(-0.5 "" 0 "" 1 "" 1.5 "", nolabels noticks) title("Standard", size(medium)) xsize(2.2) plotregion(ilcolor(black) ilwidth(thick) ilpattern(solid))

{text}{p 2 6 2}Variables that uniquely identify margins: treatment1{p_end}
{res}{txt}
{com}. 
. *Control group 12/04/2016-02/05/2016
. svy: regress st_terrori c.st_ethno##i.control_1 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       508
{txt}{col 1}Number of PSUs{col 20}= {res}      145{txt}{col 49}Population size{col 67}={res} 481.785308
{txt}{col 49}Design df{col 67}= {res}       144
{txt}{col 49}F({res}  13{txt},{res}    132{txt}){col 67}= {res}      5.48
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1115

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2617243{col 48}{space 2} .0755919{col 59}{space 1}    3.46{col 68}{space 3}0.001{col 76}{space 4} .1123112{col 89}{space 3} .4111373
{txt}{space 23}1.control_1 {c |}{col 36}{res}{space 2}-.0220958{col 48}{space 2} .0584466{col 59}{space 1}   -0.38{col 68}{space 3}0.706{col 76}{space 4}-.1376199{col 89}{space 3} .0934283
{txt}{space 34} {c |}
{space 14}control_1#c.st_ethno {c |}
{space 32}1  {c |}{col 36}{res}{space 2}  .313409{col 48}{space 2}  .127419{col 59}{space 1}    2.46{col 68}{space 3}0.015{col 76}{space 4} .0615558{col 89}{space 3} .5652622
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0414043{col 48}{space 2} .0245717{col 59}{space 1}    1.69{col 68}{space 3}0.094{col 76}{space 4}-.0071635{col 89}{space 3} .0899722
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}-.0146175{col 48}{space 2} .2476115{col 59}{space 1}   -0.06{col 68}{space 3}0.953{col 76}{space 4}-.5040402{col 89}{space 3} .4748052
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} .1370044{col 48}{space 2} .2985146{col 59}{space 1}    0.46{col 68}{space 3}0.647{col 76}{space 4}-.4530321{col 89}{space 3} .7270409
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0389031{col 48}{space 2} .0566412{col 59}{space 1}   -0.69{col 68}{space 3}0.493{col 76}{space 4}-.1508587{col 89}{space 3} .0730524
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0666808{col 48}{space 2}  .055951{col 59}{space 1}   -1.19{col 68}{space 3}0.235{col 76}{space 4}-.1772721{col 89}{space 3} .0439105
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1053713{col 48}{space 2} .0607523{col 59}{space 1}   -1.73{col 68}{space 3}0.085{col 76}{space 4}-.2254529{col 89}{space 3} .0147102
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0624269{col 48}{space 2} .0560247{col 59}{space 1}    1.11{col 68}{space 3}0.267{col 76}{space 4}-.0483102{col 89}{space 3}  .173164
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0496747{col 48}{space 2} .0738829{col 59}{space 1}    0.67{col 68}{space 3}0.502{col 76}{space 4}-.0963603{col 89}{space 3} .1957098
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0037418{col 48}{space 2} .0755579{col 59}{space 1}   -0.05{col 68}{space 3}0.961{col 76}{space 4}-.1530876{col 89}{space 3} .1456039
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0991012{col 48}{space 2} .0899011{col 59}{space 1}    1.10{col 68}{space 3}0.272{col 76}{space 4}-.0785952{col 89}{space 3} .2767975
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .3917798{col 48}{space 2} .1054814{col 59}{space 1}    3.71{col 68}{space 3}0.000{col 76}{space 4}  .183288{col 89}{space 3} .6002716
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, at( control_1=(0,1) st_ethno=(.375) work2=(2) (mean) sex st_age st_age2 proedu2) vce(unconditional)
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}       508

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.375}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:control_1}{space 7}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.sex}{space 11}{txt:=} {space 4}.537274 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.sex}{space 11}{txt:=} {space 4}.462726 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age}{space 10}{txt:=} {space 3}.4446622 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age2}{space 9}{txt:=} {space 3}.2448259 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.proedu2}{space 7}{txt:=} {space 3}.0461634 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.proedu2}{space 7}{txt:=} {space 3}.4715159 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.proedu2}{space 7}{txt:=} {space 3}.2155177 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.proedu2}{space 7}{txt:=} {space 4}.266803 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:work2}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.375}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:control_1}{space 7}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.sex}{space 11}{txt:=} {space 4}.537274 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.sex}{space 11}{txt:=} {space 4}.462726 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age}{space 10}{txt:=} {space 3}.4446622 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age2}{space 9}{txt:=} {space 3}.2448259 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.proedu2}{space 7}{txt:=} {space 3}.0461634 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.proedu2}{space 7}{txt:=} {space 3}.4715159 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.proedu2}{space 7}{txt:=} {space 3}.2155177 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.proedu2}{space 7}{txt:=} {space 4}.266803 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:work2}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5377268{col 26}{space 2} .0199603{col 37}{space 1}   26.94{col 46}{space 3}0.000{col 54}{space 4} .4982737{col 67}{space 3} .5771799
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6331594{col 26}{space 2} .0344255{col 37}{space 1}   18.39{col 46}{space 3}0.000{col 54}{space 4} .5651147{col 67}{space 3}  .701204
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, xdimension(control_1) name(graph_c1, replace) recast(scatter) ytitle("") ylabel(0.4(0.05)0.8, nolabels grid) xtitle("") xlabel(-0.5 "" 0 "" 1 "" 1.5 "", nolabels noticks) title("Alternative 1") xsize(2.2)

{text}{p 2 6 2}Variables that uniquely identify margins: control_1{p_end}
{res}{txt}
{com}. 
. *Control group 03/05/2016-22/05/2016                            
. svy: regress st_terrori c.st_ethno##i.control_2 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       292
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 285.326403
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      4.59
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1651

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2507096{col 48}{space 2}  .106433{col 59}{space 1}    2.36{col 68}{space 3}0.020{col 76}{space 4} .0399056{col 89}{space 3} .4615135
{txt}{space 23}1.control_2 {c |}{col 36}{res}{space 2}-.1081812{col 48}{space 2} .0710443{col 59}{space 1}   -1.52{col 68}{space 3}0.131{col 76}{space 4}-.2488934{col 89}{space 3}  .032531
{txt}{space 34} {c |}
{space 14}control_2#c.st_ethno {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .5064353{col 48}{space 2} .1717991{col 59}{space 1}    2.95{col 68}{space 3}0.004{col 76}{space 4} .1661655{col 89}{space 3} .8467051
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0221596{col 48}{space 2} .0269077{col 59}{space 1}   -0.82{col 68}{space 3}0.412{col 76}{space 4}-.0754537{col 89}{space 3} .0311344
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}-.5532563{col 48}{space 2} .2735828{col 59}{space 1}   -2.02{col 68}{space 3}0.045{col 76}{space 4}-1.095122{col 89}{space 3} -.011391
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} .4395954{col 48}{space 2} .3295986{col 59}{space 1}    1.33{col 68}{space 3}0.185{col 76}{space 4}-.2132162{col 89}{space 3} 1.092407
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} .0936633{col 48}{space 2} .0666975{col 59}{space 1}    1.40{col 68}{space 3}0.163{col 76}{space 4}-.0384394{col 89}{space 3} .2257661
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0676947{col 48}{space 2} .0664601{col 59}{space 1}    1.02{col 68}{space 3}0.311{col 76}{space 4}-.0639379{col 89}{space 3} .1993273
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} .0664845{col 48}{space 2}  .066875{col 59}{space 1}    0.99{col 68}{space 3}0.322{col 76}{space 4}-.0659698{col 89}{space 3} .1989389
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .1073825{col 48}{space 2} .0685704{col 59}{space 1}    1.57{col 68}{space 3}0.120{col 76}{space 4}-.0284298{col 89}{space 3} .2431949
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0301684{col 48}{space 2} .0747494{col 59}{space 1}    0.40{col 68}{space 3}0.687{col 76}{space 4}-.1178822{col 89}{space 3} .1782189
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1531214{col 48}{space 2} .0923762{col 59}{space 1}    1.66{col 68}{space 3}0.100{col 76}{space 4}-.0298413{col 89}{space 3} .3360841
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0162595{col 48}{space 2} .1110374{col 59}{space 1}   -0.15{col 68}{space 3}0.884{col 76}{space 4} -.236183{col 89}{space 3}  .203664
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .4496433{col 48}{space 2} .1051321{col 59}{space 1}    4.28{col 68}{space 3}0.000{col 76}{space 4} .2414159{col 89}{space 3} .6578707
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, at( control_2=(0,1) st_ethno=(.375) work2=(2) (mean) sex st_age st_age2 proedu2) vce(unconditional)
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}       292

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.375}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:control_2}{space 7}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.sex}{space 11}{txt:=} {space 3}.4833447 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.sex}{space 11}{txt:=} {space 3}.5166553 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age}{space 10}{txt:=} {space 3}.4211191 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age2}{space 9}{txt:=} {space 3}.2254206 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.proedu2}{space 7}{txt:=} {space 3}.1069351 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.proedu2}{space 7}{txt:=} {space 3}.4159137 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.proedu2}{space 7}{txt:=} {space 3}.2369992 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.proedu2}{space 7}{txt:=} {space 4}.240152 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:work2}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.375}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:control_2}{space 7}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.sex}{space 11}{txt:=} {space 3}.4833447 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.sex}{space 11}{txt:=} {space 3}.5166553 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age}{space 10}{txt:=} {space 3}.4211191 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age2}{space 9}{txt:=} {space 3}.2254206 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.proedu2}{space 7}{txt:=} {space 3}.1069351 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.proedu2}{space 7}{txt:=} {space 3}.4159137 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.proedu2}{space 7}{txt:=} {space 3}.2369992 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.proedu2}{space 7}{txt:=} {space 4}.240152 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:work2}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  .576666{col 26}{space 2} .0246083{col 37}{space 1}   23.43{col 46}{space 3}0.000{col 54}{space 4} .5279262{col 67}{space 3} .6254058
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .658398{col 26}{space 2} .0419809{col 37}{space 1}   15.68{col 46}{space 3}0.000{col 54}{space 4} .5752495{col 67}{space 3} .7415465
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, xdimension(control_2) name(graph_c2, replace) recast(scatter) ytitle("") ylabel(0.4(0.05)0.8, nolabels grid) xtitle("") xlabel(-0.5 "" 0 "" 1 "" 1.5 "", nolabels noticks) title("Alternative 2") xsize(2.2)

{text}{p 2 6 2}Variables that uniquely identify margins: control_2{p_end}
{res}{txt}
{com}. 
. *Control group 23/05/2016-11/06/2016
. svy: regress st_terrori c.st_ethno##i.control_3 i.sex st_age st_age2 i.proedu2 i.work2 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       161
{txt}{col 1}Number of PSUs{col 20}= {res}       84{txt}{col 49}Population size{col 67}={res} 163.622272
{txt}{col 49}Design df{col 67}= {res}        83
{txt}{col 49}F({res}  13{txt},{res}     71{txt}){col 67}= {res}     10.03
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.3122

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .4144023{col 48}{space 2} .1217666{col 59}{space 1}    3.40{col 68}{space 3}0.001{col 76}{space 4} .1722135{col 89}{space 3} .6565911
{txt}{space 23}1.control_3 {c |}{col 36}{res}{space 2} .0199309{col 48}{space 2} .0655576{col 59}{space 1}    0.30{col 68}{space 3}0.762{col 76}{space 4}-.1104606{col 89}{space 3} .1503224
{txt}{space 34} {c |}
{space 14}control_3#c.st_ethno {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .2481069{col 48}{space 2} .1546356{col 59}{space 1}    1.60{col 68}{space 3}0.112{col 76}{space 4} -.059457{col 89}{space 3} .5556709
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}  .055578{col 48}{space 2} .0453334{col 59}{space 1}    1.23{col 68}{space 3}0.224{col 76}{space 4}-.0345882{col 89}{space 3} .1457443
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .0991584{col 48}{space 2} .3538658{col 59}{space 1}    0.28{col 68}{space 3}0.780{col 76}{space 4}-.6046665{col 89}{space 3} .8029832
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6593105{col 48}{space 2} .4552901{col 59}{space 1}   -1.45{col 68}{space 3}0.151{col 76}{space 4}-1.564864{col 89}{space 3} .2462433
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.2178471{col 48}{space 2}  .093795{col 59}{space 1}   -2.32{col 68}{space 3}0.023{col 76}{space 4}-.4044016{col 89}{space 3}-.0312927
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1460788{col 48}{space 2} .0952026{col 59}{space 1}   -1.53{col 68}{space 3}0.129{col 76}{space 4}-.3354329{col 89}{space 3} .0432753
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} -.242277{col 48}{space 2}  .094358{col 59}{space 1}   -2.57{col 68}{space 3}0.012{col 76}{space 4}-.4299514{col 89}{space 3}-.0546027
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0322243{col 48}{space 2} .1592046{col 59}{space 1}   -0.20{col 68}{space 3}0.840{col 76}{space 4} -.348876{col 89}{space 3} .2844273
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .1416285{col 48}{space 2} .1736817{col 59}{space 1}    0.82{col 68}{space 3}0.417{col 76}{space 4}-.2038173{col 89}{space 3} .4870744
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0094399{col 48}{space 2} .1727038{col 59}{space 1}    0.05{col 68}{space 3}0.957{col 76}{space 4} -.334061{col 89}{space 3} .3529408
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.2044494{col 48}{space 2} .1701305{col 59}{space 1}   -1.20{col 68}{space 3}0.233{col 76}{space 4} -.542832{col 89}{space 3} .1339333
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .6372925{col 48}{space 2} .1916124{col 59}{space 1}    3.33{col 68}{space 3}0.001{col 76}{space 4} .2561831{col 89}{space 3} 1.018402
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, at( control_3=(0,1) st_ethno=(.375) work2=(2) (mean) sex st_age st_age2 proedu2) vce(unconditional)
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}       161

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.375}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:control_3}{space 7}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.sex}{space 11}{txt:=} {space 3}.5263925 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.sex}{space 11}{txt:=} {space 3}.4736075 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age}{space 10}{txt:=} {space 3}.3888768 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age2}{space 9}{txt:=} {space 3}.2050521 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.proedu2}{space 7}{txt:=} {space 3}.0560446 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.proedu2}{space 7}{txt:=} {space 3}.4972511 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.proedu2}{space 7}{txt:=} {space 3}.2168329 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.proedu2}{space 7}{txt:=} {space 3}.2298715 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:work2}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:st_ethno}{space 8}{txt:=} {space 7}.375}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:control_3}{space 7}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.sex}{space 11}{txt:=} {space 3}.5263925 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.sex}{space 11}{txt:=} {space 3}.4736075 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age}{space 10}{txt:=} {space 3}.3888768 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:st_age2}{space 9}{txt:=} {space 3}.2050521 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.proedu2}{space 7}{txt:=} {space 3}.0560446 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.proedu2}{space 7}{txt:=} {space 3}.4972511 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.proedu2}{space 7}{txt:=} {space 3}.2168329 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.proedu2}{space 7}{txt:=} {space 3}.2298715 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:work2}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4944666{col 26}{space 2} .0303276{col 37}{space 1}   16.30{col 46}{space 3}0.000{col 54}{space 4} .4341462{col 67}{space 3}  .554787
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6074377{col 26}{space 2} .0451694{col 37}{space 1}   13.45{col 46}{space 3}0.000{col 54}{space 4} .5175976{col 67}{space 3} .6972777
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, xdimension(control_3) name(graph_c3, replace) recast(scatter) ytitle("") ylabel(0.4(0.05)0.8, nolabels grid) xtitle("") xlabel(-0.5 "" 0 "" 1 "" 1.5 "", nolabels noticks) title("Alternative 3") xsize(2.2)

{text}{p 2 6 2}Variables that uniquely identify margins: control_3{p_end}
{res}{txt}
{com}. 
. grc1leg graph_t1 graph_c1 graph_c2 graph_c3, cols(4) ycommon graphregion(fcolor(white) lcolor(white)) iscale(0.9) xsize(7) imargin(0 0 0 0) graphregion(margin(medium))
{res}{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
{txt}end of do-file

{com}. do "Appendix_S.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. keep if german==1 & governmentsplit!=.                                  
{txt}(1,898 observations deleted)

{com}. 
. *-------------------------------------------------------------------------------
. *Appendix S: Additional robustness check: Results without weights 
. *-------------------------------------------------------------------------------
. 
. 
.                                                                         *Table S1*
. 
. *Full ethnocentrism index and civil liberties with reference to terrorism
. reg st_terrori c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=., vce(robust)

{txt}Linear regression                               Number of obs     = {res}       254
                                                {txt}F(11, 242)        =  {res}     2.23
                                                {txt}Prob > F          = {res}    0.0135
                                                {txt}R-squared         = {res}    0.0826
                                                {txt}Root MSE          =    {res} .25875

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1865767{col 48}{space 2} .0978737{col 59}{space 1}    1.91{col 68}{space 3}0.058{col 76}{space 4}-.0062165{col 89}{space 3} .3793698
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0064884{col 48}{space 2}  .034722{col 59}{space 1}   -0.19{col 68}{space 3}0.852{col 76}{space 4}-.0748844{col 89}{space 3} .0619076
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .722811{col 48}{space 2} .3149438{col 59}{space 1}    2.30{col 68}{space 3}0.023{col 76}{space 4}   .10243{col 89}{space 3} 1.343192
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6724732{col 48}{space 2}  .372338{col 59}{space 1}   -1.81{col 68}{space 3}0.072{col 76}{space 4} -1.40591{col 89}{space 3} .0609637
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1279642{col 48}{space 2}  .069011{col 59}{space 1}   -1.85{col 68}{space 3}0.065{col 76}{space 4}-.2639032{col 89}{space 3} .0079747
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0570226{col 48}{space 2} .0736602{col 59}{space 1}   -0.77{col 68}{space 3}0.440{col 76}{space 4}-.2021195{col 89}{space 3} .0880743
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1243008{col 48}{space 2} .0746355{col 59}{space 1}   -1.67{col 68}{space 3}0.097{col 76}{space 4} -.271319{col 89}{space 3} .0227173
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0441189{col 48}{space 2} .0752099{col 59}{space 1}   -0.59{col 68}{space 3}0.558{col 76}{space 4}-.1922685{col 89}{space 3} .1040307
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0716037{col 48}{space 2} .0905342{col 59}{space 1}   -0.79{col 68}{space 3}0.430{col 76}{space 4}-.2499393{col 89}{space 3}  .106732
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0114799{col 48}{space 2} .1177535{col 59}{space 1}    0.10{col 68}{space 3}0.922{col 76}{space 4}-.2204727{col 89}{space 3} .2434324
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0657204{col 48}{space 2} .1120577{col 59}{space 1}   -0.59{col 68}{space 3}0.558{col 76}{space 4}-.2864534{col 89}{space 3} .1550126
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .4947937{col 48}{space 2} .1115974{col 59}{space 1}    4.43{col 68}{space 3}0.000{col 76}{space 4} .2749675{col 89}{space 3} .7146199
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. reg st_terrori c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=., vce(robust) 

{txt}Linear regression                               Number of obs     = {res}       254
                                                {txt}F(12, 241)        =  {res}     2.73
                                                {txt}Prob > F          = {res}    0.0017
                                                {txt}R-squared         = {res}    0.1123
                                                {txt}Root MSE          =    {res} .25505

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .1741339{col 48}{space 2} .0964618{col 59}{space 1}    1.81{col 68}{space 3}0.072{col 76}{space 4} -.015882{col 89}{space 3} .3641497
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1154279{col 48}{space 2}  .038112{col 59}{space 1}    3.03{col 68}{space 3}0.003{col 76}{space 4} .0403527{col 89}{space 3} .1905031
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}   -.0162{col 48}{space 2} .0342971{col 59}{space 1}   -0.47{col 68}{space 3}0.637{col 76}{space 4}-.0837604{col 89}{space 3} .0513604
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .740494{col 48}{space 2} .3097988{col 59}{space 1}    2.39{col 68}{space 3}0.018{col 76}{space 4} .1302349{col 89}{space 3} 1.350753
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.7166124{col 48}{space 2} .3613593{col 59}{space 1}   -1.98{col 68}{space 3}0.048{col 76}{space 4}-1.428438{col 89}{space 3}-.0047866
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1239894{col 48}{space 2} .0644344{col 59}{space 1}   -1.92{col 68}{space 3}0.055{col 76}{space 4}-.2509159{col 89}{space 3} .0029372
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0493159{col 48}{space 2} .0698576{col 59}{space 1}   -0.71{col 68}{space 3}0.481{col 76}{space 4}-.1869253{col 89}{space 3} .0882936
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1333605{col 48}{space 2} .0700645{col 59}{space 1}   -1.90{col 68}{space 3}0.058{col 76}{space 4}-.2713775{col 89}{space 3} .0046564
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0555632{col 48}{space 2} .0785326{col 59}{space 1}   -0.71{col 68}{space 3}0.480{col 76}{space 4}-.2102611{col 89}{space 3} .0991346
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0671845{col 48}{space 2}   .09331{col 59}{space 1}   -0.72{col 68}{space 3}0.472{col 76}{space 4}-.2509918{col 89}{space 3} .1166229
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}  .033704{col 48}{space 2} .1198087{col 59}{space 1}    0.28{col 68}{space 3}0.779{col 76}{space 4}-.2023019{col 89}{space 3} .2697099
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0685186{col 48}{space 2} .1130989{col 59}{space 1}   -0.61{col 68}{space 3}0.545{col 76}{space 4}-.2913072{col 89}{space 3} .1542701
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .4863478{col 48}{space 2} .1112171{col 59}{space 1}    4.37{col 68}{space 3}0.000{col 76}{space 4} .2672661{col 89}{space 3} .7054295
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. reg st_terrori c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=., vce(robust) 

{txt}Linear regression                               Number of obs     = {res}       254
                                                {txt}F(13, 240)        =  {res}     4.80
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1476
                                                {txt}Root MSE          =    {res} .25046

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0486204{col 48}{space 2} .1072289{col 59}{space 1}    0.45{col 68}{space 3}0.651{col 76}{space 4}-.1626095{col 89}{space 3} .2598503
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1339675{col 48}{space 2}  .076574{col 59}{space 1}   -1.75{col 68}{space 3}0.081{col 76}{space 4}-.2848104{col 89}{space 3} .0168754
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6093883{col 48}{space 2} .1670855{col 59}{space 1}    3.65{col 68}{space 3}0.000{col 76}{space 4}  .280247{col 89}{space 3} .9385297
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0121796{col 48}{space 2} .0337955{col 59}{space 1}   -0.36{col 68}{space 3}0.719{col 76}{space 4}-.0787533{col 89}{space 3} .0543941
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .8313232{col 48}{space 2} .3028543{col 59}{space 1}    2.74{col 68}{space 3}0.007{col 76}{space 4} .2347312{col 89}{space 3} 1.427915
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.8458131{col 48}{space 2} .3534799{col 59}{space 1}   -2.39{col 68}{space 3}0.017{col 76}{space 4}-1.542132{col 89}{space 3}-.1494939
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1096075{col 48}{space 2} .0624871{col 59}{space 1}   -1.75{col 68}{space 3}0.081{col 76}{space 4}-.2327007{col 89}{space 3} .0134856
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0265041{col 48}{space 2} .0687347{col 59}{space 1}   -0.39{col 68}{space 3}0.700{col 76}{space 4}-.1619045{col 89}{space 3} .1088963
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1027761{col 48}{space 2} .0677732{col 59}{space 1}   -1.52{col 68}{space 3}0.131{col 76}{space 4}-.2362823{col 89}{space 3} .0307301
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0741476{col 48}{space 2} .0710623{col 59}{space 1}   -1.04{col 68}{space 3}0.298{col 76}{space 4} -.214133{col 89}{space 3} .0658378
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0831188{col 48}{space 2} .0845353{col 59}{space 1}   -0.98{col 68}{space 3}0.326{col 76}{space 4}-.2496448{col 89}{space 3} .0834071
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0162355{col 48}{space 2} .1165212{col 59}{space 1}    0.14{col 68}{space 3}0.889{col 76}{space 4}-.2132993{col 89}{space 3} .2457703
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0871945{col 48}{space 2} .1098069{col 59}{space 1}   -0.79{col 68}{space 3}0.428{col 76}{space 4}-.3035029{col 89}{space 3} .1291138
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}  .523689{col 48}{space 2} .1091221{col 59}{space 1}    4.80{col 68}{space 3}0.000{col 76}{space 4} .3087297{col 89}{space 3} .7386484
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. *Full ethnocentrism index and civil liberties without reference to terrorism
. reg st_surveillanceindex c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=., vce(robust) 

{txt}Linear regression                               Number of obs     = {res}       249
                                                {txt}F(11, 237)        =  {res}     4.46
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1398
                                                {txt}Root MSE          =    {res} .22342

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}              st_surveillanceindex{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2254016{col 48}{space 2} .0869597{col 59}{space 1}    2.59{col 68}{space 3}0.010{col 76}{space 4}  .054089{col 89}{space 3} .3967142
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0726813{col 48}{space 2} .0303644{col 59}{space 1}   -2.39{col 68}{space 3}0.017{col 76}{space 4}-.1324999{col 89}{space 3}-.0128627
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5950575{col 48}{space 2} .2618579{col 59}{space 1}    2.27{col 68}{space 3}0.024{col 76}{space 4} .0791913{col 89}{space 3} 1.110924
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.4697672{col 48}{space 2} .2934934{col 59}{space 1}   -1.60{col 68}{space 3}0.111{col 76}{space 4}-1.047956{col 89}{space 3} .1084218
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0365588{col 48}{space 2} .0563527{col 59}{space 1}   -0.65{col 68}{space 3}0.517{col 76}{space 4} -.147575{col 89}{space 3} .0744575
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0446267{col 48}{space 2} .0616907{col 59}{space 1}    0.72{col 68}{space 3}0.470{col 76}{space 4}-.0769055{col 89}{space 3} .1661589
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0452368{col 48}{space 2} .0613096{col 59}{space 1}   -0.74{col 68}{space 3}0.461{col 76}{space 4}-.1660181{col 89}{space 3} .0755445
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0491093{col 48}{space 2} .0725638{col 59}{space 1}    0.68{col 68}{space 3}0.499{col 76}{space 4}-.0938432{col 89}{space 3} .1920617
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0487594{col 48}{space 2} .0829318{col 59}{space 1}    0.59{col 68}{space 3}0.557{col 76}{space 4}-.1146182{col 89}{space 3}  .212137
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1537641{col 48}{space 2} .1035163{col 59}{space 1}    1.49{col 68}{space 3}0.139{col 76}{space 4}-.0501654{col 89}{space 3} .3576937
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0659781{col 48}{space 2} .0794201{col 59}{space 1}    0.83{col 68}{space 3}0.407{col 76}{space 4}-.0904815{col 89}{space 3} .2224377
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .1871653{col 48}{space 2} .0908523{col 59}{space 1}    2.06{col 68}{space 3}0.040{col 76}{space 4} .0081841{col 89}{space 3} .3661465
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. reg st_surveillanceindex c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=., vce(robust) 

{txt}Linear regression                               Number of obs     = {res}       249
                                                {txt}F(12, 236)        =  {res}     5.97
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1749
                                                {txt}Root MSE          =    {res} .21929

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}              st_surveillanceindex{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2159983{col 48}{space 2} .0871785{col 59}{space 1}    2.48{col 68}{space 3}0.014{col 76}{space 4} .0442508{col 89}{space 3} .3877458
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1126959{col 48}{space 2}  .034512{col 59}{space 1}    3.27{col 68}{space 3}0.001{col 76}{space 4}  .044705{col 89}{space 3} .1806868
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0775205{col 48}{space 2} .0297959{col 59}{space 1}   -2.60{col 68}{space 3}0.010{col 76}{space 4}-.1362205{col 89}{space 3}-.0188205
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6379374{col 48}{space 2} .2566183{col 59}{space 1}    2.49{col 68}{space 3}0.014{col 76}{space 4} .1323821{col 89}{space 3} 1.143493
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5372378{col 48}{space 2} .2911893{col 59}{space 1}   -1.84{col 68}{space 3}0.066{col 76}{space 4}  -1.1109{col 89}{space 3} .0364246
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} -.037255{col 48}{space 2} .0542362{col 59}{space 1}   -0.69{col 68}{space 3}0.493{col 76}{space 4}-.1441039{col 89}{space 3} .0695938
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0467054{col 48}{space 2} .0590747{col 59}{space 1}    0.79{col 68}{space 3}0.430{col 76}{space 4}-.0696757{col 89}{space 3} .1630865
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0574518{col 48}{space 2} .0593003{col 59}{space 1}   -0.97{col 68}{space 3}0.334{col 76}{space 4}-.1742773{col 89}{space 3} .0593737
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0413257{col 48}{space 2} .0772819{col 59}{space 1}    0.53{col 68}{space 3}0.593{col 76}{space 4}-.1109248{col 89}{space 3} .1935763
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0516406{col 48}{space 2} .0872613{col 59}{space 1}    0.59{col 68}{space 3}0.555{col 76}{space 4}-.1202701{col 89}{space 3} .2235512
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1734149{col 48}{space 2} .1074445{col 59}{space 1}    1.61{col 68}{space 3}0.108{col 76}{space 4}-.0382579{col 89}{space 3} .3850877
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0700525{col 48}{space 2}  .082344{col 59}{space 1}    0.85{col 68}{space 3}0.396{col 76}{space 4}-.0921707{col 89}{space 3} .2322758
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .1739896{col 48}{space 2} .0918685{col 59}{space 1}    1.89{col 68}{space 3}0.059{col 76}{space 4}-.0069975{col 89}{space 3} .3549768
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. reg st_surveillanceindex c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=., vce(robust) 

{txt}Linear regression                               Number of obs     = {res}       249
                                                {txt}F(13, 235)        =  {res}     5.49
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1749
                                                {txt}Root MSE          =    {res} .21975

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}              st_surveillanceindex{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .2168623{col 48}{space 2}  .095951{col 59}{space 1}    2.26{col 68}{space 3}0.025{col 76}{space 4} .0278282{col 89}{space 3} .4058963
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1143571{col 48}{space 2} .0801225{col 59}{space 1}    1.43{col 68}{space 3}0.155{col 76}{space 4} -.043493{col 89}{space 3} .2722072
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.0041705{col 48}{space 2} .1783166{col 59}{space 1}   -0.02{col 68}{space 3}0.981{col 76}{space 4}-.3554738{col 89}{space 3} .3471327
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0775538{col 48}{space 2} .0300042{col 59}{space 1}   -2.58{col 68}{space 3}0.010{col 76}{space 4}-.1366654{col 89}{space 3}-.0184421
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6372596{col 48}{space 2} .2565645{col 59}{space 1}    2.48{col 68}{space 3}0.014{col 76}{space 4} .1317993{col 89}{space 3}  1.14272
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} -.536346{col 48}{space 2} .2912134{col 59}{space 1}   -1.84{col 68}{space 3}0.067{col 76}{space 4}-1.110069{col 89}{space 3} .0373766
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0373225{col 48}{space 2} .0542973{col 59}{space 1}   -0.69{col 68}{space 3}0.493{col 76}{space 4}-.1442942{col 89}{space 3} .0696492
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0466012{col 48}{space 2}  .059407{col 59}{space 1}    0.78{col 68}{space 3}0.434{col 76}{space 4}-.0704371{col 89}{space 3} .1636395
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0576074{col 48}{space 2} .0589514{col 59}{space 1}   -0.98{col 68}{space 3}0.329{col 76}{space 4}-.1737482{col 89}{space 3} .0585333
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0414385{col 48}{space 2} .0772731{col 59}{space 1}    0.54{col 68}{space 3}0.592{col 76}{space 4}-.1107979{col 89}{space 3}  .193675
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0517671{col 48}{space 2} .0873251{col 59}{space 1}    0.59{col 68}{space 3}0.554{col 76}{space 4}-.1202729{col 89}{space 3} .2238071
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .1735361{col 48}{space 2} .1073898{col 59}{space 1}    1.62{col 68}{space 3}0.107{col 76}{space 4}-.0380336{col 89}{space 3} .3851059
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0701944{col 48}{space 2} .0825688{col 59}{space 1}    0.85{col 68}{space 3}0.396{col 76}{space 4}-.0924752{col 89}{space 3}  .232864
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .1737312{col 48}{space 2} .0927204{col 59}{space 1}    1.87{col 68}{space 3}0.062{col 76}{space 4}-.0089381{col 89}{space 3} .3564005
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. *Authorchild and civil liberties with reference to terrorism
. reg st_terrori c.st_authorchild i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=., vce(robust) 

{txt}Linear regression                               Number of obs     = {res}       271
                                                {txt}F(11, 259)        =  {res}     1.88
                                                {txt}Prob > F          = {res}    0.0428
                                                {txt}R-squared         = {res}    0.0570
                                                {txt}Root MSE          =    {res} .26276

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.0279854{col 48}{space 2}  .070034{col 59}{space 1}   -0.40{col 68}{space 3}0.690{col 76}{space 4}-.1658938{col 89}{space 3} .1099231
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0007988{col 48}{space 2} .0340134{col 59}{space 1}    0.02{col 68}{space 3}0.981{col 76}{space 4}-.0661791{col 89}{space 3} .0677767
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5293728{col 48}{space 2} .2873152{col 59}{space 1}    1.84{col 68}{space 3}0.067{col 76}{space 4}-.0363983{col 89}{space 3} 1.095144
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.4138313{col 48}{space 2} .3477268{col 59}{space 1}   -1.19{col 68}{space 3}0.235{col 76}{space 4}-1.098563{col 89}{space 3} .2709003
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1620639{col 48}{space 2} .0691144{col 59}{space 1}   -2.34{col 68}{space 3}0.020{col 76}{space 4}-.2981616{col 89}{space 3}-.0259661
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1076684{col 48}{space 2} .0752396{col 59}{space 1}   -1.43{col 68}{space 3}0.154{col 76}{space 4}-.2558276{col 89}{space 3} .0404909
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1786084{col 48}{space 2} .0740311{col 59}{space 1}   -2.41{col 68}{space 3}0.017{col 76}{space 4}-.3243879{col 89}{space 3}-.0328288
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0026307{col 48}{space 2} .0747309{col 59}{space 1}   -0.04{col 68}{space 3}0.972{col 76}{space 4}-.1497881{col 89}{space 3} .1445267
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0330913{col 48}{space 2} .0898613{col 59}{space 1}   -0.37{col 68}{space 3}0.713{col 76}{space 4}-.2100432{col 89}{space 3} .1438605
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0472178{col 48}{space 2} .1165991{col 59}{space 1}    0.40{col 68}{space 3}0.686{col 76}{space 4}-.1823851{col 89}{space 3} .2768207
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0560144{col 48}{space 2} .1108647{col 59}{space 1}   -0.51{col 68}{space 3}0.614{col 76}{space 4}-.2743254{col 89}{space 3} .1622965
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5924525{col 48}{space 2} .0961064{col 59}{space 1}    6.16{col 68}{space 3}0.000{col 76}{space 4} .4032031{col 89}{space 3}  .781702
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. reg st_terrori c.st_authorchild i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=., vce(robust) 

{txt}Linear regression                               Number of obs     = {res}       271
                                                {txt}F(12, 258)        =  {res}     2.30
                                                {txt}Prob > F          = {res}    0.0084
                                                {txt}R-squared         = {res}    0.0821
                                                {txt}Root MSE          =    {res} .25974

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.0431176{col 48}{space 2} .0690119{col 59}{space 1}   -0.62{col 68}{space 3}0.533{col 76}{space 4}-.1790159{col 89}{space 3} .0927806
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}  .106246{col 48}{space 2} .0387623{col 59}{space 1}    2.74{col 68}{space 3}0.007{col 76}{space 4} .0299152{col 89}{space 3} .1825768
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0087519{col 48}{space 2} .0339845{col 59}{space 1}   -0.26{col 68}{space 3}0.797{col 76}{space 4}-.0756743{col 89}{space 3} .0581704
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .546069{col 48}{space 2} .2818302{col 59}{space 1}    1.94{col 68}{space 3}0.054{col 76}{space 4}-.0089115{col 89}{space 3} 1.101049
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.4515792{col 48}{space 2} .3379771{col 59}{space 1}   -1.34{col 68}{space 3}0.183{col 76}{space 4}-1.117124{col 89}{space 3} .2139657
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1600831{col 48}{space 2} .0649725{col 59}{space 1}   -2.46{col 68}{space 3}0.014{col 76}{space 4}-.2880271{col 89}{space 3}-.0321391
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1037126{col 48}{space 2} .0718419{col 59}{space 1}   -1.44{col 68}{space 3}0.150{col 76}{space 4}-.2451838{col 89}{space 3} .0377585
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1889504{col 48}{space 2} .0699654{col 59}{space 1}   -2.70{col 68}{space 3}0.007{col 76}{space 4}-.3267263{col 89}{space 3}-.0511745
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0102252{col 48}{space 2} .0757133{col 59}{space 1}   -0.14{col 68}{space 3}0.893{col 76}{space 4}  -.15932{col 89}{space 3} .1388696
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0273534{col 48}{space 2} .0908507{col 59}{space 1}   -0.30{col 68}{space 3}0.764{col 76}{space 4}-.2062568{col 89}{space 3}   .15155
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0687382{col 48}{space 2} .1173508{col 59}{space 1}    0.59{col 68}{space 3}0.559{col 76}{space 4}-.1623492{col 89}{space 3} .2998256
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} -.052583{col 48}{space 2} .1110169{col 59}{space 1}   -0.47{col 68}{space 3}0.636{col 76}{space 4}-.2711978{col 89}{space 3} .1660317
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5815003{col 48}{space 2} .0955369{col 59}{space 1}    6.09{col 68}{space 3}0.000{col 76}{space 4}  .393369{col 89}{space 3} .7696317
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. reg st_terrori c.st_authorchild##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=., vce(robust) 

{txt}Linear regression                               Number of obs     = {res}       271
                                                {txt}F(13, 257)        =  {res}     2.22
                                                {txt}Prob > F          = {res}    0.0091
                                                {txt}R-squared         = {res}    0.0849
                                                {txt}Root MSE          =    {res} .25984

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.0788406{col 48}{space 2} .0826731{col 59}{space 1}   -0.95{col 68}{space 3}0.341{col 76}{space 4}-.2416435{col 89}{space 3} .0839623
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}  .078387{col 48}{space 2} .0495219{col 59}{space 1}    1.58{col 68}{space 3}0.115{col 76}{space 4}-.0191334{col 89}{space 3} .1759075
{txt}{space 34} {c |}
{space 7}treatment1#c.st_authorchild {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1326987{col 48}{space 2} .1410528{col 59}{space 1}    0.94{col 68}{space 3}0.348{col 76}{space 4}-.1450677{col 89}{space 3} .4104652
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0106875{col 48}{space 2} .0338863{col 59}{space 1}   -0.32{col 68}{space 3}0.753{col 76}{space 4}-.0774175{col 89}{space 3} .0560426
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5459536{col 48}{space 2} .2816831{col 59}{space 1}    1.94{col 68}{space 3}0.054{col 76}{space 4}-.0087473{col 89}{space 3} 1.100655
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2} -.460898{col 48}{space 2} .3384612{col 59}{space 1}   -1.36{col 68}{space 3}0.174{col 76}{space 4}-1.127408{col 89}{space 3} .2056125
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1623344{col 48}{space 2} .0647765{col 59}{space 1}   -2.51{col 68}{space 3}0.013{col 76}{space 4}-.2898947{col 89}{space 3}-.0347741
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.1059483{col 48}{space 2} .0716954{col 59}{space 1}   -1.48{col 68}{space 3}0.141{col 76}{space 4}-.2471336{col 89}{space 3}  .035237
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1863489{col 48}{space 2} .0697602{col 59}{space 1}   -2.67{col 68}{space 3}0.008{col 76}{space 4}-.3237232{col 89}{space 3}-.0489745
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0145736{col 48}{space 2} .0762559{col 59}{space 1}   -0.19{col 68}{space 3}0.849{col 76}{space 4}-.1647395{col 89}{space 3} .1355924
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0311294{col 48}{space 2}  .090813{col 59}{space 1}   -0.34{col 68}{space 3}0.732{col 76}{space 4}-.2099618{col 89}{space 3}  .147703
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0620833{col 48}{space 2} .1174959{col 59}{space 1}    0.53{col 68}{space 3}0.598{col 76}{space 4}-.1692941{col 89}{space 3} .2934607
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0622239{col 48}{space 2} .1134565{col 59}{space 1}   -0.55{col 68}{space 3}0.584{col 76}{space 4}-.2856467{col 89}{space 3} .1611989
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5959863{col 48}{space 2} .0971884{col 59}{space 1}    6.13{col 68}{space 3}0.000{col 76}{space 4} .4045992{col 89}{space 3} .7873733
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. *Interaction model including both ethnocentrism and authoritarianism
. reg st_terrori c.st_ethno##i.treatment1 c.st_authorchild##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2, vce(robust) 

{txt}Linear regression                               Number of obs     = {res}       254
                                                {txt}F(15, 238)        =  {res}     5.11
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1605
                                                {txt}Root MSE          =    {res} .24959

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}st_ethno {c |}{col 36}{res}{space 2} .0629331{col 48}{space 2} .1090586{col 59}{space 1}    0.58{col 68}{space 3}0.564{col 76}{space 4}-.1519102{col 89}{space 3} .2777765
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1504769{col 48}{space 2} .0780101{col 59}{space 1}   -1.93{col 68}{space 3}0.055{col 76}{space 4}-.3041553{col 89}{space 3} .0032015
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .7158871{col 48}{space 2} .1791484{col 59}{space 1}    4.00{col 68}{space 3}0.000{col 76}{space 4} .3629681{col 89}{space 3} 1.068806
{txt}{space 34} {c |}
{space 20}st_authorchild {c |}{col 36}{res}{space 2}-.1014788{col 48}{space 2}  .084196{col 59}{space 1}   -1.21{col 68}{space 3}0.229{col 76}{space 4}-.2673435{col 89}{space 3} .0643858
{txt}{space 34} {c |}
{space 7}treatment1#c.st_authorchild {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1011061{col 48}{space 2} .1478856{col 59}{space 1}   -0.68{col 68}{space 3}0.495{col 76}{space 4} -.392438{col 89}{space 3} .1902259
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0130033{col 48}{space 2} .0335198{col 59}{space 1}   -0.39{col 68}{space 3}0.698{col 76}{space 4}-.0790367{col 89}{space 3} .0530301
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .807419{col 48}{space 2} .2961763{col 59}{space 1}    2.73{col 68}{space 3}0.007{col 76}{space 4}  .223957{col 89}{space 3} 1.390881
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.7909671{col 48}{space 2} .3447156{col 59}{space 1}   -2.29{col 68}{space 3}0.023{col 76}{space 4}-1.470051{col 89}{space 3}-.1118837
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1093862{col 48}{space 2} .0614397{col 59}{space 1}   -1.78{col 68}{space 3}0.076{col 76}{space 4}-.2304213{col 89}{space 3} .0116488
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0278288{col 48}{space 2} .0679131{col 59}{space 1}   -0.41{col 68}{space 3}0.682{col 76}{space 4}-.1616163{col 89}{space 3} .1059588
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1015381{col 48}{space 2} .0661363{col 59}{space 1}   -1.54{col 68}{space 3}0.126{col 76}{space 4}-.2318255{col 89}{space 3} .0287492
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0746192{col 48}{space 2} .0727605{col 59}{space 1}   -1.03{col 68}{space 3}0.306{col 76}{space 4} -.217956{col 89}{space 3} .0687177
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0885295{col 48}{space 2} .0851704{col 59}{space 1}   -1.04{col 68}{space 3}0.300{col 76}{space 4}-.2563136{col 89}{space 3} .0792545
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0084449{col 48}{space 2} .1165989{col 59}{space 1}    0.07{col 68}{space 3}0.942{col 76}{space 4}-.2212527{col 89}{space 3} .2381425
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0670723{col 48}{space 2} .1155582{col 59}{space 1}   -0.58{col 68}{space 3}0.562{col 76}{space 4}-.2947198{col 89}{space 3} .1605751
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5331175{col 48}{space 2} .1105237{col 59}{space 1}    4.82{col 68}{space 3}0.000{col 76}{space 4} .3153878{col 89}{space 3} .7508472
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. 
. 
{txt}end of do-file

{com}. do "Appendix_T.do"
{txt}
{com}. ** Replication Code for:
. ** Terrorism Activates Ethnocentrism to Explain Greater Willingness to 
. ** Sacrifice Civil Liberties: Evidence from Germany
. ** Authors: Christina Novak Hansen and Peter Thisted Dinesen 
. ** Date: 10/02/2021
. 
. *-------------------------------------------------------------------------------
. 
. clear all
{txt}
{com}. use GGSS2016_analysis.dta
{txt}(ALLBUS 2016)

{com}. svyset xs11 [pweight=wghtpew], vce(linearized)

      {txt}pweight:{col 16}{res}wghtpew
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}xs11
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}

{com}. 
. keep if german==1 & governmentsplit!=.  
{txt}(1,898 observations deleted)

{com}. 
. 
. *-------------------------------------------------------------------------------
. *Appendix T: Additional robustness check: The influence of living in Bavaria
. *-------------------------------------------------------------------------------
. 
. /*The following alayses are based on the variables included in the main models 
> (Table 1, Model 1-3)*/.
{txt}
{com}. 
. 
. 
.                 
. *-------------------------------------------------------------------------------
. 
. *Which federal states are represented in the pre-attack and post-attacks groups?
. tab land treatment1, column
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

  BUNDESLAND, IN DEM {c |}      treatment1
   BEFRAGTE<R> WOHNT {c |}   Control  Treatment {c |}     Total
{hline 21}{c +}{hline 22}{c +}{hline 10}
  SCHLESWIG-HOLSTEIN {c |}{res}         8          3 {txt}{c |}{res}        11 
                     {txt}{c |}{res}      3.36       5.08 {txt}{c |}{res}      3.70 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
             HAMBURG {c |}{res}         3          2 {txt}{c |}{res}         5 
                     {txt}{c |}{res}      1.26       3.39 {txt}{c |}{res}      1.68 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
       NIEDERSACHSEN {c |}{res}        22          6 {txt}{c |}{res}        28 
                     {txt}{c |}{res}      9.24      10.17 {txt}{c |}{res}      9.43 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
              BREMEN {c |}{res}         2          1 {txt}{c |}{res}         3 
                     {txt}{c |}{res}      0.84       1.69 {txt}{c |}{res}      1.01 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
 NORDRHEIN-WESTFALEN {c |}{res}        37          8 {txt}{c |}{res}        45 
                     {txt}{c |}{res}     15.55      13.56 {txt}{c |}{res}     15.15 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
              HESSEN {c |}{res}        20          0 {txt}{c |}{res}        20 
                     {txt}{c |}{res}      8.40       0.00 {txt}{c |}{res}      6.73 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
     RHEINLAND-PFALZ {c |}{res}        13          0 {txt}{c |}{res}        13 
                     {txt}{c |}{res}      5.46       0.00 {txt}{c |}{res}      4.38 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
  BADEN-WUERTTEMBERG {c |}{res}        24          4 {txt}{c |}{res}        28 
                     {txt}{c |}{res}     10.08       6.78 {txt}{c |}{res}      9.43 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
              BAYERN {c |}{res}        23          6 {txt}{c |}{res}        29 
                     {txt}{c |}{res}      9.66      10.17 {txt}{c |}{res}      9.76 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
            SAARLAND {c |}{res}         0          1 {txt}{c |}{res}         1 
                     {txt}{c |}{res}      0.00       1.69 {txt}{c |}{res}      0.34 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
   EHEM. BERLIN-WEST {c |}{res}         4          2 {txt}{c |}{res}         6 
                     {txt}{c |}{res}      1.68       3.39 {txt}{c |}{res}      2.02 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
    EHEM. BERLIN-OST {c |}{res}         5          6 {txt}{c |}{res}        11 
                     {txt}{c |}{res}      2.10      10.17 {txt}{c |}{res}      3.70 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
         BRANDENBURG {c |}{res}        13          7 {txt}{c |}{res}        20 
                     {txt}{c |}{res}      5.46      11.86 {txt}{c |}{res}      6.73 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
MECKLENB.-VORPOMMERN {c |}{res}         6          0 {txt}{c |}{res}         6 
                     {txt}{c |}{res}      2.52       0.00 {txt}{c |}{res}      2.02 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
             SACHSEN {c |}{res}        29          4 {txt}{c |}{res}        33 
                     {txt}{c |}{res}     12.18       6.78 {txt}{c |}{res}     11.11 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
      SACHSEN-ANHALT {c |}{res}        12          6 {txt}{c |}{res}        18 
                     {txt}{c |}{res}      5.04      10.17 {txt}{c |}{res}      6.06 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
          THUERINGEN {c |}{res}        17          3 {txt}{c |}{res}        20 
                     {txt}{c |}{res}      7.14       5.08 {txt}{c |}{res}      6.73 
{txt}{hline 21}{c +}{hline 22}{c +}{hline 10}
               Total {c |}{res}       238         59 {txt}{c |}{res}       297 
                     {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab statedummy treatment1

            {txt}{c |}      treatment1
 statedummy {c |}   Control  Treatment {c |}     Total
{hline 12}{c +}{hline 22}{c +}{hline 10}
Not Bavaria {c |}{res}       215         53 {txt}{c |}{res}       268 
{txt}    Bavaria {c |}{res}        23          6 {txt}{c |}{res}        29 
{txt}{hline 12}{c +}{hline 22}{c +}{hline 10}
      Total {c |}{res}       238         59 {txt}{c |}{res}       297 

{txt}
{com}. 
.         ****Main regression controlling for the influence of living in Bavaria***
.                                                                 *Table T1*
.                                                         
. *The following regressions account for the influence of living in Bavaria
. *Full ethnocentrism index and civil liberties with reference to terrorism
. svy: regress st_terrori statedummy c.st_ethno i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=.
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  12{txt},{res}    105{txt}){col 67}= {res}      1.83
{txt}{col 49}Prob > F{col 67}= {res}    0.0524
{txt}{col 49}R-squared{col 67}= {res}    0.0898

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}statedummy {c |}{col 36}{res}{space 2} -.124711{col 48}{space 2} .0550162{col 59}{space 1}   -2.27{col 68}{space 3}0.025{col 76}{space 4}-.2336775{col 89}{space 3}-.0157446
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .1569327{col 48}{space 2} .0909883{col 59}{space 1}    1.72{col 68}{space 3}0.087{col 76}{space 4}-.0232811{col 89}{space 3} .3371465
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0032078{col 48}{space 2} .0377506{col 59}{space 1}    0.08{col 68}{space 3}0.932{col 76}{space 4} -.071562{col 89}{space 3} .0779775
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5293191{col 48}{space 2} .3364002{col 59}{space 1}    1.57{col 68}{space 3}0.118{col 76}{space 4} -.136964{col 89}{space 3} 1.195602
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.4695076{col 48}{space 2} .3755127{col 59}{space 1}   -1.25{col 68}{space 3}0.214{col 76}{space 4}-1.213258{col 89}{space 3} .2742427
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1173053{col 48}{space 2} .0727417{col 59}{space 1}   -1.61{col 68}{space 3}0.110{col 76}{space 4}-.2613794{col 89}{space 3} .0267689
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0530945{col 48}{space 2}  .084986{col 59}{space 1}   -0.62{col 68}{space 3}0.533{col 76}{space 4}-.2214201{col 89}{space 3} .1152311
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1053789{col 48}{space 2} .0845535{col 59}{space 1}   -1.25{col 68}{space 3}0.215{col 76}{space 4}-.2728478{col 89}{space 3}   .06209
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0255359{col 48}{space 2} .0789647{col 59}{space 1}   -0.32{col 68}{space 3}0.747{col 76}{space 4}-.1819354{col 89}{space 3} .1308636
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} -.051278{col 48}{space 2} .0971185{col 59}{space 1}   -0.53{col 68}{space 3}0.599{col 76}{space 4}-.2436335{col 89}{space 3} .1410775
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0210545{col 48}{space 2} .1226069{col 59}{space 1}    0.17{col 68}{space 3}0.864{col 76}{space 4} -.221784{col 89}{space 3}  .263893
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0707576{col 48}{space 2} .1208288{col 59}{space 1}   -0.59{col 68}{space 3}0.559{col 76}{space 4}-.3100744{col 89}{space 3} .1685591
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5171018{col 48}{space 2}  .111316{col 59}{space 1}    4.65{col 68}{space 3}0.000{col 76}{space 4} .2966263{col 89}{space 3} .7375772
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori statedummy c.st_ethno i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  13{txt},{res}    104{txt}){col 67}= {res}      2.29
{txt}{col 49}Prob > F{col 67}= {res}    0.0106
{txt}{col 49}R-squared{col 67}= {res}    0.1170

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}statedummy {c |}{col 36}{res}{space 2}-.1240361{col 48}{space 2} .0517153{col 59}{space 1}   -2.40{col 68}{space 3}0.018{col 76}{space 4}-.2264646{col 89}{space 3}-.0216075
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .1474503{col 48}{space 2} .0895271{col 59}{space 1}    1.65{col 68}{space 3}0.102{col 76}{space 4}-.0298695{col 89}{space 3} .3247701
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .1133903{col 48}{space 2} .0394718{col 59}{space 1}    2.87{col 68}{space 3}0.005{col 76}{space 4} .0352114{col 89}{space 3} .1915692
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0050353{col 48}{space 2} .0378653{col 59}{space 1}   -0.13{col 68}{space 3}0.894{col 76}{space 4}-.0800323{col 89}{space 3} .0699616
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .5520932{col 48}{space 2} .3300814{col 59}{space 1}    1.67{col 68}{space 3}0.097{col 76}{space 4}-.1016747{col 89}{space 3} 1.205861
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.5109198{col 48}{space 2} .3644433{col 59}{space 1}   -1.40{col 68}{space 3}0.164{col 76}{space 4}-1.232746{col 89}{space 3} .2109061
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.1109142{col 48}{space 2} .0681008{col 59}{space 1}   -1.63{col 68}{space 3}0.106{col 76}{space 4}-.2457965{col 89}{space 3}  .023968
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0455042{col 48}{space 2} .0825919{col 59}{space 1}   -0.55{col 68}{space 3}0.583{col 76}{space 4}-.2090879{col 89}{space 3} .1180795
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.1143805{col 48}{space 2} .0816742{col 59}{space 1}   -1.40{col 68}{space 3}0.164{col 76}{space 4}-.2761466{col 89}{space 3} .0473856
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0401328{col 48}{space 2} .0829541{col 59}{space 1}   -0.48{col 68}{space 3}0.629{col 76}{space 4}-.2044338{col 89}{space 3} .1241683
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0501115{col 48}{space 2} .1016677{col 59}{space 1}   -0.49{col 68}{space 3}0.623{col 76}{space 4}-.2514772{col 89}{space 3} .1512541
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0370412{col 48}{space 2} .1261823{col 59}{space 1}    0.29{col 68}{space 3}0.770{col 76}{space 4}-.2128787{col 89}{space 3} .2869611
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0782189{col 48}{space 2} .1207873{col 59}{space 1}   -0.65{col 68}{space 3}0.519{col 76}{space 4}-.3174533{col 89}{space 3} .1610154
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5073247{col 48}{space 2} .1094456{col 59}{space 1}    4.64{col 68}{space 3}0.000{col 76}{space 4} .2905538{col 89}{space 3} .7240956
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. svy: regress st_terrori statedummy c.st_ethno##i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if treatment1!=. 
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  14{txt},{res}    103{txt}){col 67}= {res}      5.12
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1511

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                        st_terrori{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}statedummy {c |}{col 36}{res}{space 2}-.1082545{col 48}{space 2}  .052351{col 59}{space 1}   -2.07{col 68}{space 3}0.041{col 76}{space 4}-.2119423{col 89}{space 3}-.0045667
{txt}{space 26}st_ethno {c |}{col 36}{res}{space 2} .0166924{col 48}{space 2} .0969826{col 59}{space 1}    0.17{col 68}{space 3}0.864{col 76}{space 4}-.1753938{col 89}{space 3} .2087787
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.1332105{col 48}{space 2} .0728053{col 59}{space 1}   -1.83{col 68}{space 3}0.070{col 76}{space 4}-.2774105{col 89}{space 3} .0109895
{txt}{space 34} {c |}
{space 13}treatment1#c.st_ethno {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .6265585{col 48}{space 2} .1562217{col 59}{space 1}    4.01{col 68}{space 3}0.000{col 76}{space 4} .3171417{col 89}{space 3} .9359754
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0014194{col 48}{space 2} .0364658{col 59}{space 1}   -0.04{col 68}{space 3}0.969{col 76}{space 4}-.0736445{col 89}{space 3} .0708058
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .6454548{col 48}{space 2} .3185183{col 59}{space 1}    2.03{col 68}{space 3}0.045{col 76}{space 4} .0145891{col 89}{space 3} 1.276321
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.6125865{col 48}{space 2} .3606386{col 59}{space 1}   -1.70{col 68}{space 3}0.092{col 76}{space 4}-1.326877{col 89}{space 3} .1017037
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0936875{col 48}{space 2} .0666754{col 59}{space 1}   -1.41{col 68}{space 3}0.163{col 76}{space 4}-.2257466{col 89}{space 3} .0383715
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} -.019494{col 48}{space 2} .0812289{col 59}{space 1}   -0.24{col 68}{space 3}0.811{col 76}{space 4}-.1803781{col 89}{space 3} .1413901
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0916397{col 48}{space 2} .0787584{col 59}{space 1}   -1.16{col 68}{space 3}0.247{col 76}{space 4}-.2476306{col 89}{space 3} .0643512
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0667859{col 48}{space 2} .0729037{col 59}{space 1}   -0.92{col 68}{space 3}0.362{col 76}{space 4}-.2111808{col 89}{space 3}  .077609
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} -.081784{col 48}{space 2}  .091922{col 59}{space 1}   -0.89{col 68}{space 3}0.375{col 76}{space 4}-.2638471{col 89}{space 3} .1002792
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0077715{col 48}{space 2} .1197281{col 59}{space 1}    0.06{col 68}{space 3}0.948{col 76}{space 4} -.229365{col 89}{space 3}  .244908
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} -.097859{col 48}{space 2} .1167298{col 59}{space 1}   -0.84{col 68}{space 3}0.404{col 76}{space 4}-.3290571{col 89}{space 3}  .133339
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .5458421{col 48}{space 2} .1051044{col 59}{space 1}    5.19{col 68}{space 3}0.000{col 76}{space 4} .3376696{col 89}{space 3} .7540147
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, drop(i.sex st_age st_age2 i.proedu2 i.work2) nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. 
. *-------------------------------------------------------------------------------
. 
. 
. 
.                         ****Regressions predicting number of contacts attempts***
.                                                                 *Table T2*
. 
. *Number of contacts via telephone (xs08)
. svy: regress st_phone statedummy i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if st_terrori!=. & st_ethno!=.
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  12{txt},{res}    105{txt}){col 67}= {res}      2.04
{txt}{col 49}Prob > F{col 67}= {res}    0.0277
{txt}{col 49}R-squared{col 67}= {res}    0.1117

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                          st_phone{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}statedummy {c |}{col 36}{res}{space 2} .0456536{col 48}{space 2} .0284061{col 59}{space 1}    1.61{col 68}{space 3}0.111{col 76}{space 4}-.0106082{col 89}{space 3} .1019154
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}  .018839{col 48}{space 2}  .013285{col 59}{space 1}    1.42{col 68}{space 3}0.159{col 76}{space 4}-.0074736{col 89}{space 3} .0451515
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0149176{col 48}{space 2} .0117133{col 59}{space 1}    1.27{col 68}{space 3}0.205{col 76}{space 4} -.008282{col 89}{space 3} .0381173
{txt}{space 28}st_age {c |}{col 36}{res}{space 2}  .021657{col 48}{space 2}  .068303{col 59}{space 1}    0.32{col 68}{space 3}0.752{col 76}{space 4}-.1136257{col 89}{space 3} .1569398
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.0118586{col 48}{space 2}    .0879{col 59}{space 1}   -0.13{col 68}{space 3}0.893{col 76}{space 4}-.1859557{col 89}{space 3} .1622385
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0331813{col 48}{space 2} .0247272{col 59}{space 1}   -1.34{col 68}{space 3}0.182{col 76}{space 4}-.0821566{col 89}{space 3}  .015794
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2}-.0063966{col 48}{space 2} .0278706{col 59}{space 1}   -0.23{col 68}{space 3}0.819{col 76}{space 4}-.0615978{col 89}{space 3} .0488047
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0329884{col 48}{space 2} .0259486{col 59}{space 1}   -1.27{col 68}{space 3}0.206{col 76}{space 4}-.0843829{col 89}{space 3} .0184061
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0414133{col 48}{space 2} .0129282{col 59}{space 1}    3.20{col 68}{space 3}0.002{col 76}{space 4} .0158074{col 89}{space 3} .0670192
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0220824{col 48}{space 2} .0145284{col 59}{space 1}    1.52{col 68}{space 3}0.131{col 76}{space 4} -.006693{col 89}{space 3} .0508578
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0280884{col 48}{space 2} .0264542{col 59}{space 1}    1.06{col 68}{space 3}0.291{col 76}{space 4}-.0243075{col 89}{space 3} .0804843
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0014509{col 48}{space 2} .0153704{col 59}{space 1}    0.09{col 68}{space 3}0.925{col 76}{space 4}-.0289921{col 89}{space 3}  .031894
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .0060557{col 48}{space 2} .0180413{col 59}{space 1}    0.34{col 68}{space 3}0.738{col 76}{space 4}-.0296774{col 89}{space 3} .0417889
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label replace 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. *Number of contacts via visits (xs09)
. svy: regress st_housevisits statedummy i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if st_terrori!=. & st_ethno!=.
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  12{txt},{res}    105{txt}){col 67}= {res}      4.31
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0486

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                    st_housevisits{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}statedummy {c |}{col 36}{res}{space 2} .0035363{col 48}{space 2} .0150726{col 59}{space 1}    0.23{col 68}{space 3}0.815{col 76}{space 4} -.026317{col 89}{space 3} .0333896
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2} .0373277{col 48}{space 2}  .013128{col 59}{space 1}    2.84{col 68}{space 3}0.005{col 76}{space 4}  .011326{col 89}{space 3} .0633295
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2} .0059555{col 48}{space 2} .0174355{col 59}{space 1}    0.34{col 68}{space 3}0.733{col 76}{space 4}-.0285776{col 89}{space 3} .0404887
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .1960328{col 48}{space 2} .1059648{col 59}{space 1}    1.85{col 68}{space 3}0.067{col 76}{space 4}-.0138438{col 89}{space 3} .4059094
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.2875886{col 48}{space 2} .1421316{col 59}{space 1}   -2.02{col 68}{space 3}0.045{col 76}{space 4}-.5690982{col 89}{space 3} -.006079
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2} .0218564{col 48}{space 2} .0237157{col 59}{space 1}    0.92{col 68}{space 3}0.359{col 76}{space 4}-.0251156{col 89}{space 3} .0688284
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0237837{col 48}{space 2} .0266335{col 59}{space 1}    0.89{col 68}{space 3}0.374{col 76}{space 4}-.0289673{col 89}{space 3} .0765348
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2} .0053602{col 48}{space 2} .0256912{col 59}{space 1}    0.21{col 68}{space 3}0.835{col 76}{space 4}-.0455245{col 89}{space 3} .0562449
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2}-.0193534{col 48}{space 2} .0331082{col 59}{space 1}   -0.58{col 68}{space 3}0.560{col 76}{space 4}-.0849284{col 89}{space 3} .0462216
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .0069542{col 48}{space 2} .0278166{col 59}{space 1}    0.25{col 68}{space 3}0.803{col 76}{space 4}  -.04814{col 89}{space 3} .0620485
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2}-.0706816{col 48}{space 2}  .030916{col 59}{space 1}   -2.29{col 68}{space 3}0.024{col 76}{space 4}-.1319147{col 89}{space 3}-.0094485
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2}-.0000463{col 48}{space 2} .0404363{col 59}{space 1}   -0.00{col 68}{space 3}0.999{col 76}{space 4}-.0801356{col 89}{space 3} .0800429
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .0321934{col 48}{space 2} .0307326{col 59}{space 1}    1.05{col 68}{space 3}0.297{col 76}{space 4}-.0286764{col 89}{space 3} .0930632
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append 
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. *Number of contacts, other (xs13)
. svy: regress st_othercontacts statedummy i.treatment1 i.sex st_age st_age2 i.proedu2 i.work2 if st_terrori!=. & st_ethno!=.
{txt}(running regress on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 49}Number of obs{col 67}= {res}       254
{txt}{col 1}Number of PSUs{col 20}= {res}      117{txt}{col 49}Population size{col 67}={res} 251.049568
{txt}{col 49}Design df{col 67}= {res}       116
{txt}{col 49}F({res}  12{txt},{res}    105{txt}){col 67}= {res}      0.18
{txt}{col 49}Prob > F{col 67}= {res}    0.9991
{txt}{col 49}R-squared{col 67}= {res}    0.0784

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}  Linearized
{col 1}                  st_othercontacts{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      t{col 68}   P>|t|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}statedummy {c |}{col 36}{res}{space 2}  .009627{col 48}{space 2} .0098071{col 59}{space 1}    0.98{col 68}{space 3}0.328{col 76}{space 4}-.0097972{col 89}{space 3} .0290512
{txt}{space 34} {c |}
{space 24}treatment1 {c |}
{space 24}Treatment  {c |}{col 36}{res}{space 2}-.0025754{col 48}{space 2} .0019485{col 59}{space 1}   -1.32{col 68}{space 3}0.189{col 76}{space 4}-.0064346{col 89}{space 3} .0012838
{txt}{space 34} {c |}
{space 31}sex {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0048719{col 48}{space 2} .0032908{col 59}{space 1}   -1.48{col 68}{space 3}0.141{col 76}{space 4}-.0113897{col 89}{space 3}  .001646
{txt}{space 28}st_age {c |}{col 36}{res}{space 2} .0349203{col 48}{space 2} .0252091{col 59}{space 1}    1.39{col 68}{space 3}0.169{col 76}{space 4}-.0150094{col 89}{space 3}   .08485
{txt}{space 27}st_age2 {c |}{col 36}{res}{space 2}-.0279755{col 48}{space 2}  .021256{col 59}{space 1}   -1.32{col 68}{space 3}0.191{col 76}{space 4}-.0700757{col 89}{space 3} .0141247
{txt}{space 34} {c |}
{space 27}proedu2 {c |}
{space 18}Upper secondary  {c |}{col 36}{res}{space 2}-.0035021{col 48}{space 2}  .002574{col 59}{space 1}   -1.36{col 68}{space 3}0.176{col 76}{space 4}-.0086003{col 89}{space 3} .0015961
{txt}{space 9}Short tertiary education  {c |}{col 36}{res}{space 2} .0083335{col 48}{space 2} .0059067{col 59}{space 1}    1.41{col 68}{space 3}0.161{col 76}{space 4}-.0033654{col 89}{space 3} .0200324
{txt}Medium to long tertiary education  {c |}{col 36}{res}{space 2}-.0055552{col 48}{space 2} .0041675{col 59}{space 1}   -1.33{col 68}{space 3}0.185{col 76}{space 4}-.0138095{col 89}{space 3} .0026992
{txt}{space 34} {c |}
{space 29}work2 {c |}
{space 26}Working  {c |}{col 36}{res}{space 2} .0018429{col 48}{space 2} .0021053{col 59}{space 1}    0.88{col 68}{space 3}0.383{col 76}{space 4}-.0023268{col 89}{space 3} .0060127
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2}-.0047839{col 48}{space 2}  .004786{col 59}{space 1}   -1.00{col 68}{space 3}0.320{col 76}{space 4}-.0142631{col 89}{space 3} .0046954
{txt}{space 24}Housework  {c |}{col 36}{res}{space 2} .0009225{col 48}{space 2} .0021049{col 59}{space 1}    0.44{col 68}{space 3}0.662{col 76}{space 4}-.0032466{col 89}{space 3} .0050916
{txt}{space 13}In school or student  {c |}{col 36}{res}{space 2} .0012221{col 48}{space 2} .0028291{col 59}{space 1}    0.43{col 68}{space 3}0.667{col 76}{space 4}-.0043812{col 89}{space 3} .0068255
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}-.0026057{col 48}{space 2} .0027756{col 59}{space 1}   -0.94{col 68}{space 3}0.350{col 76}{space 4}-.0081032{col 89}{space 3} .0028919
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using results.xls, nocons stats(coef se) dec(2) symbol(**,*) alpha(0.01,0.05) label append
{txt}{browse `"results.xls"'}
{browse `"C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission"' :dir}{com} : {txt}{stata `"seeout using "results.txt", label"':seeout}

{com}. 
. 
. *-------------------------------------------------------------------------------
. 
{txt}end of do-file

{com}.                         
.                                 
.                                 
. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\CNovakhansen\Dropbox\2. Political Science\Artikel_ethno_2019\PSRM\Revision 1\Data for submission\Hansen&Dinesen_Log-file_PSRM.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}18 Jun 2021, 11:49:37
{txt}{.-}
{smcl}
{txt}{sf}{ul off}